diff --git a/.gitignore b/.gitignore index 2bf53ff1..7c6db0c0 100644 --- a/.gitignore +++ b/.gitignore @@ -79,7 +79,7 @@ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: -# .python-version +.python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. @@ -175,3 +175,8 @@ logs/ runs/ wandb/ .gradio/ + +autotune/mot17/ +autotune/pretrained/ +autotune/*.zip +*.local.* diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 26d2a64b..7bc2c332 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -48,7 +48,7 @@ repos: additional_dependencies: - "mdformat-mkdocs[recommended]>=2.1.0" - "mdformat-ruff" - args: ["--number"] + args: ["--number", "--wrap=no"] - repo: https://github.com/codespell-project/codespell rev: v2.4.2 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 9e8aca5c..9782ed9a 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -2,129 +2,81 @@ ## Our Pledge -We as members, contributors, and leaders pledge to make participation in our -community a harassment-free experience for everyone, regardless of age, body -size, visible or invisible disability, ethnicity, sex characteristics, gender -identity and expression, level of experience, education, socioeconomic status, -nationality, personal appearance, race, caste, color, religion, or sexual -identity and orientation. +We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socioeconomic status, nationality, personal appearance, race, caste, color, religion, or sexual identity and orientation. -We pledge to act and interact in ways that contribute to an open, welcoming, -diverse, inclusive, and healthy community. +We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. ## Our Standards -Examples of behavior that contributes to a positive environment for our -community include: +Examples of behavior that contributes to a positive environment for our community include: - 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Permanent Ban -**Community Impact**: Demonstrating a pattern of violation of community -standards, including sustained inappropriate behavior, harassment of an -individual, or aggression toward or disparagement of classes of individuals. +**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. -**Consequence**: A permanent ban from any sort of public interaction within the -community. +**Consequence**: A permanent ban from any sort of public interaction within the community. ## Attribution -This Code of Conduct is adapted from the [Contributor Covenant][homepage], -version 2.1, available at -[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 2.1, available at [https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1]. -Community Impact Guidelines were inspired by -[Mozilla's code of conduct enforcement ladder][mozilla coc]. +Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder][mozilla coc]. -For answers to common questions about this code of conduct, see the FAQ at -[https://www.contributor-covenant.org/faq][faq]. Translations are available at -[https://www.contributor-covenant.org/translations][translations]. +For answers to common questions about this code of conduct, see the FAQ at [https://www.contributor-covenant.org/faq][faq]. Translations are available at [https://www.contributor-covenant.org/translations][translations]. [faq]: https://www.contributor-covenant.org/faq [homepage]: https://www.contributor-covenant.org diff --git a/README.md b/README.md index 951c2006..e504f2d4 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,7 @@

trackers

Plug-and-play multi-object tracking for any detection model.

-[![version](https://badge.fury.io/py/trackers.svg)](https://badge.fury.io/py/trackers) -[![downloads](https://img.shields.io/pypi/dm/trackers)](https://pypistats.org/packages/trackers) -[![license](https://img.shields.io/badge/license-Apache%202.0-blue)](https://github.com/roboflow/trackers/blob/release/stable/LICENSE.md) -[![python-version](https://img.shields.io/pypi/pyversions/trackers)](https://badge.fury.io/py/trackers) -[![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) -[![discord](https://img.shields.io/discord/1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk) +[![version](https://badge.fury.io/py/trackers.svg)](https://badge.fury.io/py/trackers) [![downloads](https://img.shields.io/pypi/dm/trackers)](https://pypistats.org/packages/trackers) [![license](https://img.shields.io/badge/license-Apache%202.0-blue)](https://github.com/roboflow/trackers/blob/release/stable/LICENSE.md) [![python-version](https://img.shields.io/pypi/pyversions/trackers)](https://badge.fury.io/py/trackers) [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-objects-with-bytetrack-tracker.ipynb) [![discord](https://img.shields.io/discord/1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk) @@ -79,13 +74,13 @@ https://github.com/user-attachments/assets/d2347a25-469d-44cd-8049-d15274bd91ae ## Algorithms -Clean, modular implementations of leading trackers. All HOTA scores use default parameters. +Clean, modular implementations of leading trackers. HOTA scores at default parameters; tuned scores use Optuna-optimised hyperparameters from the autotune campaign (MOT17-SDP). -| Algorithm | Description | MOT17 HOTA | SportsMOT HOTA | SoccerNet HOTA | DanceTrack HOTA | -| :-------------------------------------------: | :-------------------------------------------------------------: | :--------: | :------------: | :------------: | :-------------: | -| [SORT](https://arxiv.org/abs/1602.00763) | Kalman filter + Hungarian matching baseline. | 58.4 | 70.9 | 81.6 | 45.0 | -| [ByteTrack](https://arxiv.org/abs/2110.06864) | Two-stage association using high and low confidence detections. | 60.1 | **73.0** | **84.0** | 50.2 | -| [OC-SORT](https://arxiv.org/abs/2203.14360) | Observation-centric recovery for lost tracks. | **61.9** | 71.7 | 78.4 | **51.8** | +| Algorithm | Description | MOT17 HOTA | MOT17 HOTA (tuned) | SportsMOT HOTA | SoccerNet HOTA | DanceTrack HOTA | +| :-------------------------------------------: | :-------------------------------------------------------------: | :--------: | :----------------: | :------------: | :------------: | :-------------: | +| [SORT](https://arxiv.org/abs/1602.00763) | Kalman filter + Hungarian matching baseline. | 55.7 | **57.7** | 70.9 | 81.6 | 45.0 | +| [ByteTrack](https://arxiv.org/abs/2110.06864) | Two-stage association using high and low confidence detections. | 60.1 | 59.1 | **73.0** | **84.0** | 50.2 | +| [OC-SORT](https://arxiv.org/abs/2203.14360) | Observation-centric recovery for lost tracks. | **61.9** | **58.9** | 71.7 | 78.4 | **51.8** | For detailed benchmarks and tuned configurations, see the [tracker comparison](https://trackers.roboflow.com/develop/trackers/comparison/). diff --git a/autotune/Dockerfile b/autotune/Dockerfile new file mode 100644 index 00000000..a8b070fc --- /dev/null +++ b/autotune/Dockerfile @@ -0,0 +1,49 @@ +# ------------------------------------------------------------------------ +# Trackers — optimize sandbox image +# Used by /optimize run --docker to run metric/guard in an isolated +# container with a read-only project mount. +# +# Build (run from project root): +# docker build -f autotune/Dockerfile -t trackers-optimize:latest . +# +# Runtime mounts (added automatically by the campaign framework): +# -v $(pwd):/workspace:ro project source (read-only) +# -v $(pwd)/.experiments:/workspace/.experiments:rw +# -v $HOME/.cache/trackers:/root/.cache/trackers:ro MOT17 data +# --tmpfs /tmp:rw,size=256m +# -w /workspace +# ------------------------------------------------------------------------ + +FROM ghcr.io/astral-sh/uv:python3.10-bookworm-slim + +# System libraries required by opencv-python +RUN apt-get update && apt-get install -y --no-install-recommends \ + libgl1 \ + libglib2.0-0 \ + libsm6 \ + libxrender1 \ + libxext6 \ + git \ + cmake \ + build-essential \ + && rm -rf /var/lib/apt/lists/* + +# Copy the full project so uv can resolve and lock all dependencies. +# The venv is installed at /opt/venv (not /workspace) so that the +# runtime read-only project mount does not shadow the Linux venv. +COPY . /build/ +WORKDIR /build + +ENV UV_PROJECT_ENVIRONMENT=/opt/venv +RUN uv sync --group optimize + +# At runtime the live project source is mounted at /workspace. +# UV_PROJECT_ENVIRONMENT points uv to the pre-built Linux venv so +# `uv run python` uses it without re-syncing. +# UV_NO_SYNC=1 prevents uv from attempting to re-sync on every run. +ENV PATH="/opt/venv/bin:$PATH" +ENV PYTHONPATH="/workspace" +ENV UV_PROJECT_ENVIRONMENT=/opt/venv +ENV UV_NO_SYNC=1 + +WORKDIR /workspace diff --git a/autotune/README.md b/autotune/README.md new file mode 100644 index 00000000..1235897d --- /dev/null +++ b/autotune/README.md @@ -0,0 +1,555 @@ +# autotune + +Autonomous optimization of MOT17 trackers — SORT, ByteTrack, OC-SORT — using the [autoresearch](https://github.com/karpathy/autoresearch) pattern with [Roboflow trackers](https://github.com/roboflow/trackers). + +Point any coding agent at this folder and let it run. ~500 experiments/hour on CPU, no GPU needed. + +## Motivation + +Multi-object tracking quality depends on two largely independent axes: **algorithm design** (state representation, association logic, track lifecycle) and **hyperparameter tuning** (confidence thresholds, buffer sizes, Kalman noise scales). Most published improvements conflate the two — a well-tuned weak algorithm can outperform a poorly-tuned strong one, making it hard to know what actually matters. + +This project separates the axes. An autonomous agent iterates over structural code changes, measuring HOTA after each change at fixed default parameters. Optuna provides a second-pass validation: after a code change is accepted, a short tuning run confirms the improvement holds under optimised parameters and is not a parameter artefact. The iteration log — including all reverted changes — is the primary research artifact. + +### Why these trackers? + +Three trackers are supported: **SORT**, **ByteTrack**, and **OC-SORT**. ByteTrack is the primary campaign target — it is the simplest practically-competitive tracker (pure IoU association, constant-velocity Kalman filter, no appearance features), making it easy to isolate the effect of individual algorithmic changes. SORT serves as the simplest possible baseline. OC-SORT extends ByteTrack with observation-centric velocity updates and direction consistency, providing an upper bound for what IoU-only association can achieve. + +### Why MOT17? + +The [MOT17 benchmark](https://www.codabench.org/competitions/10049/) provides two complementary detection sources: + +- **FRCNN public detections** — bundled with the benchmark, reproducible on any machine without a GPU or API key. Weaker than modern detectors, which creates genuine headroom for algorithmic improvement. + +Additional detectors (RF-DETR, YOLO World X) are supported by `generate_detections.py`; see the Detection sources section. + +### Why HOTA? + +[HOTA](https://arxiv.org/abs/2009.07736) (Higher Order Tracking Accuracy, Luiten et al. 2021) decomposes tracking quality into detection accuracy (DetA) and association accuracy (AssA) with equal weight. MOTA is dominated by false positives/negatives and misses ID-switch quality; IDF1 is purely association-focused. HOTA is the most informative single scalar for overall tracker health and is the primary campaign metric. IDF1, MOTA, and IDSW are logged alongside it for every run. + +## Approach + +The research loop follows the autoresearch pattern: propose one change, measure it, keep improvements, revert regressions. Each committed iteration is one atomic hypothesis. The JSONL experiment log captures every attempt — failures are as informative as successes. + +``` +Human defines: research question · metric · hard boundaries +Agent decides: what to change · what to try next +``` + +Two tools govern the loop: + +| Tool | Role | +| ----------------------------------- | -------------------------------------------------------------------------------------- | +| `optimize_tracking.py --n-trials 1` | Campaign metric — evaluates default params, gives a clean code-change signal | +| `optimize_tracking.py --n-trials N` | Optuna study — warm-starts from `best_config.json`, finds best params for current code | + +The agent is free to update `optimize_tracking.py` as the tracker architecture evolves — adding parameters that newly exist, removing ones absorbed into the implementation, tightening search ranges as knowledge accumulates. + +## Detection sources + +Each detector produces a set of sequence-level sibling directories alongside the bundled FRCNN sequences. The detector is visible directly in the filesystem path, making it easy to switch between detection sources or add new ones. + +### FRCNN (bundled) + +Pre-computed FRCNN public detections are downloaded with the benchmark data: + +``` +~/.cache/trackers/mot17/val/ + MOT17-04-FRCNN/ + det/det.txt ← bundled FRCNN detections + gt/gt.txt ← ground truth (never seen at inference) + img1/ ← video frames (needed for generated detections only) +``` + +No inference required. Pass `--det-source frcnn` (default) to use these. + +### Generated detections + +Running `generate_detections.py` creates sibling directories for each sequence: + +``` +~/.cache/trackers/mot17/val/ + MOT17-04-FRCNN/ ← original (frames + bundled FRCNN dets) + MOT17-04-RFDETR/ ← created by generate_detections.py (RF-DETR-L) + det/det.txt + gt -> ../MOT17-04-FRCNN/gt ← symlink; evaluator finds ground truth here + img1 -> ../MOT17-04-FRCNN/img1 ← symlink; full sequence structure mirrored + MOT17-04-YOLOWORLD/ ← created by generate_detections.py (YOLO World X) + det/det.txt + gt -> ../MOT17-04-FRCNN/gt + img1 -> ../MOT17-04-FRCNN/img1 +``` + +The detector tag (`RFDETR`, `YOLOWORLD`, …) is auto-derived from the model name and appended to the directory. + +`generate_detections.py` supports these backends via `--model`: + +| Model flag | Tag | Backend | Notes | +| -------------------- | ----------- | ---------------- | -------------------------------------------------- | +| `rfdetr/l` (default) | `RFDETR` | Native rfdetr | RF-DETR large; weights auto-downloaded; no API key | +| `rfdetr/n/s/m` | `RFDETR` | Native rfdetr | Nano / small / medium variants; same auto-download | +| `yolo_world/l` | `YOLOWORLD` | inference-models | YOLO World L; requires `ROBOFLOW_API_KEY` | + +Override the tag with `--detector-tag` if needed. Each detector writes to its own directory so runs never overwrite each other. + +## Tracker benchmarks + +### FRCNN public detections (MOT17-val, bundled) + +`optimize_tracking.py --fast` evaluates default parameters; `--n-trials 500` is the Optuna run. IDF1/MOTA/IDSW are only recorded for the Optuna-tuned result. + +Published reference points (MOT17-val, FRCNN, IoU-only): SORT ~45–50 (estimated) · ByteTrack ~50–52 · OC-SORT ~55–57. Theoretical ceilings: SORT ~52–55 · ByteTrack ~60–65 · OC-SORT ~62–65. + +| Config | Metric | ByteTrack | OC-SORT | SORT | +| ------------------- | ------ | ----------- | ----------- | ----------- | +| Defaults | HOTA | 50.355 | 49.690 | 49.950 | +| | IDF1 | 56.600 | 56.143 | 56.088 | +| | MOTA | 47.406 | 45.858 | 46.769 | +| | IDSW | 234 | 154 | 260 | +| + Optuna (n=500) | HOTA | 51.757 | **52.218** | 51.488 | +| | IDF1 | 58.367 | 58.946 | 58.417 | +| | MOTA | 47.740 | 47.753 | 47.770 | +| | IDSW | 237 | 233 | 173 | +| + autotune + Optuna | HOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDF1 | _(pending)_ | _(pending)_ | _(pending)_ | +| | MOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDSW | _(pending)_ | _(pending)_ | _(pending)_ | + +> **Note — why is OC-SORT's FRCNN baseline below SORT?** Default params are not tuned for FRCNN dets. `minimum_iou_threshold=0.3` is conservative for noisy public detections; ByteTrack uses 0.1. Despite the lower HOTA, OC-SORT already shows 40% fewer ID switches (154 vs 260) at defaults — its direction-consistency mechanism is working. Tuned params bring all three trackers into the 51–53 HOTA range. + +### SDP public detections (MOT17-val, bundled) + +Bundled SDP detections; same ground truth as FRCNN. Full 7-sequence eval. + +| Config | Metric | ByteTrack | OC-SORT | SORT | +| ------------------- | ------ | ---------- | ---------- | ----------- | +| Defaults | HOTA | 53.941 | 53.351 | 53.217 | +| | IDF1 | 65.402 | 65.817 | 64.538 | +| | MOTA | 62.464 | 58.731 | 61.917 | +| | IDSW | 371 | 283 | 355 | +| + Optuna (n=500) | HOTA | 56.115 | **57.747** | 56.083 | +| | IDF1 | 68.077 | 70.330 | 67.517 | +| | MOTA | 65.602 | 66.215 | 65.283 | +| | IDSW | 329 | 303 | 326 | +| + autotune + Optuna | HOTA | **59.092** | **58.905** | **58.026** | +| | IDF1 | **71.993** | **71.636** | _(pending)_ | +| | MOTA | **66.977** | **66.396** | _(pending)_ | +| | IDSW | **259** | **291** | _(pending)_ | + +> SDP is stronger than FRCNN — expect single-sequence defaults around 60–65 HOTA on MOT17-04, but the 7-sequence Optuna average is lower because the benchmark includes harder sequences that pull the mean down. + +### DPM public detections (MOT17-val, bundled) + +DPM is the weakest bundled detector. Full 7-sequence eval. + +| Config | Metric | ByteTrack | OC-SORT | SORT | +| ------------------- | ------ | ----------- | ----------- | ----------- | +| Defaults | HOTA | 31.121 | 25.256 | 29.890 | +| | IDF1 | 37.897 | 30.571 | 36.308 | +| | MOTA | 26.664 | 20.662 | 25.738 | +| | IDSW | 191 | 104 | 363 | +| + Optuna (n=500) | HOTA | 33.468 | **35.238** | 31.962 | +| | IDF1 | 41.178 | 43.798 | 38.660 | +| | MOTA | 27.603 | 30.904 | 26.717 | +| | IDSW | 199 | 119 | 265 | +| + autotune + Optuna | HOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDF1 | _(pending)_ | _(pending)_ | _(pending)_ | +| | MOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDSW | _(pending)_ | _(pending)_ | _(pending)_ | + +### RF-DETR detections (MOT17-val, generated) + +RF-DETR-L (`rfdetr/l`), native backend, weights auto-downloaded. Defaults: MOT17-04 only (`--fast`). Optuna: full 7-sequence. + +| Config | Metric | ByteTrack | OC-SORT | SORT | +| ------------------- | ------ | ----------- | ----------- | ----------- | +| Defaults | HOTA | 35.759 | 33.763 | 49.606 | +| | IDF1 | 33.341 | 31.047 | 55.911 | +| | MOTA | 19.224 | 17.446 | 43.171 | +| | IDSW | 1 | 5 | 96 | +| + Optuna (n=10k) | HOTA | 44.780 | **47.311** | 44.465 | +| | IDF1 | 51.076 | 55.651 | 50.884 | +| | MOTA | 32.355 | 39.269 | 35.053 | +| | IDSW | 336 | 212 | 489 | +| + autotune + Optuna | HOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDF1 | _(pending)_ | _(pending)_ | _(pending)_ | +| | MOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDSW | _(pending)_ | _(pending)_ | _(pending)_ | + +> RF-DETR defaults (tuned for FRCNN) favour SORT — its looser thresholds match RF-DETR's score distribution better. After Optuna, OC-SORT flips to lead: direction-consistency recovers strongly once thresholds are tuned, outpacing both ByteTrack and SORT by ~2.5 HOTA. + +### YOLO World X detections (MOT17-val, generated) + +YOLO World X (`yoloworld`), generated via `generate_detections.py`. Full 7-sequence eval. + +| Config | Metric | ByteTrack | OC-SORT | SORT | +| ------------------- | ------ | ----------- | ----------- | ----------- | +| Defaults | HOTA | 33.291 | 29.565 | 41.468 | +| | IDF1 | 33.123 | 30.079 | 46.795 | +| | MOTA | 22.589 | 18.013 | 32.179 | +| | IDSW | 89 | 49 | 107 | +| + Optuna (n=500) | HOTA | 43.110 | 41.406 | **45.629** | +| | IDF1 | 47.444 | 45.546 | 52.432 | +| | MOTA | 33.143 | 31.273 | 36.962 | +| | IDSW | 126 | 107 | 171 | +| + autotune + Optuna | HOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDF1 | _(pending)_ | _(pending)_ | _(pending)_ | +| | MOTA | _(pending)_ | _(pending)_ | _(pending)_ | +| | IDSW | _(pending)_ | _(pending)_ | _(pending)_ | + +> YOLO World X with default params shows the same pattern as RF-DETR: SORT leads because its looser IoU threshold (0.3 vs 0.1) better matches the detector's score distribution. After Optuna, ByteTrack closes to within 2.5 HOTA of SORT — the gap narrows but doesn't close, suggesting a structural mismatch rather than a pure tuning issue. + +### Metric legend + +All metrics are higher-is-better except IDSW (lower is better): + +- **HOTA** — geometric mean of DetA and AssA; equal-weight composite; primary campaign metric +- **IDF1** — ID F1 score; purely association-focused; does not penalise missed detections as heavily as MOTA +- **MOTA** — combines false positives, false negatives, and ID switches; dominated by detection quality +- **IDSW** — raw ID switch count; low IDSW signals stable long-term association + +
+Measuring baselines + +```bash +cd autotune + +# FRCNN (bundled) — all three trackers +uv run python optimize_tracking.py sort frcnn +uv run python optimize_tracking.py bytetrack frcnn +uv run python optimize_tracking.py ocsort frcnn + +# SDP (bundled) — stronger than FRCNN, same gt +uv run python optimize_tracking.py sort sdp +uv run python optimize_tracking.py bytetrack sdp +uv run python optimize_tracking.py ocsort sdp + +# DPM (bundled) — weakest bundled detector +uv run python optimize_tracking.py sort dpm +uv run python optimize_tracking.py bytetrack dpm +uv run python optimize_tracking.py ocsort dpm + +# RF-DETR (requires generate_detections.py --model rfdetr/l) +uv run python optimize_tracking.py sort rfdetr +uv run python optimize_tracking.py bytetrack rfdetr +uv run python optimize_tracking.py ocsort rfdetr + +# YOLO World (requires generate_detections.py --model yolo_world/l) +uv run python optimize_tracking.py sort yoloworld +uv run python optimize_tracking.py bytetrack yoloworld +uv run python optimize_tracking.py ocsort yoloworld +``` + +
+ +## Journal + +### ByteTrack — Phase 2 Campaign (MOT17-val SDP, 20 iterations) + +**Period**: 2026-04-02 → 2026-04-04 | **Baseline**: HOTA = 56.003 | **Final**: HOTA = 59.092 (+5.51%, +3.089 pts) | **Best commit**: `9f45bda` + +Full iteration log: `_optimizations/state/20260402-233730/experiments.jsonl`. The table below shows **kept** experiments only — all 20 iterations including regressions are in the JSONL. + +#### Positive experiments + +| Iter | Change | HOTA before → after | Δ pts | Δ % | IDSW | +| ---- | --------------------------------- | ------------------- | ------ | ------ | --------- | +| i1 | ORU velocity re-estimation | 56.003 → 56.063 | +0.060 | +0.11% | 323 → 314 | +| i2b | Separate stage-2 IoU threshold | 56.063 → 56.196 | +0.133 | +0.24% | 314 → 320 | +| i5 | Stage-1 age discount | 56.196 → 56.781 | +0.585 | +1.04% | — | +| i10 | 1st calibration wave | 56.781 → 57.424 | +0.643 | +1.13% | — | +| i11 | oru_threshold 2 → 5 | 57.424 → 57.813 | +0.389 | +0.68% | — | +| i12 | 2nd calibration wave | 57.813 → 58.753 | +0.940 | +1.62% | → 297 | +| i16 | 3rd calibration wave | 58.753 → 58.862 | +0.109 | +0.19% | 297 → 269 | +| i17 | stage2_min_updates search cap fix | 58.862 → 58.961 | +0.099 | +0.17% | 269 → 266 | +| i18 | Micro-calibration (wave 1) | 58.961 → 59.031 | +0.070 | +0.12% | 266 → 262 | +| i19 | Micro-calibration (wave 2) | 59.031 → 59.092 | +0.061 | +0.10% | 262 → 259 | + +
+Experiment descriptions + +- **ORU velocity re-estimation** (i1) — on re-detection after occlusion, replay virtual predict+update cycles along an interpolated trajectory to reconstruct velocity. Borrowed from OC-SORT. +- **Separate stage-2 IoU threshold** (i2b) — stage-2 low-confidence recovery uses an independent, lower threshold than stage-1; original ByteTrack shares one threshold for both stages. Codex contribution. +- **Stage-1 age discount** (i5, `iou_age_weight`) — IoU in the Hungarian cost matrix is multiplied by `1 / (1 + w × lost_frames)` for stale tracks, biasing assignment toward recently-seen ones without affecting the threshold gate. +- **1st calibration wave** (i10) — first Optuna pass over all 9 parameters under the new code. Key shifts: `lost_track_buffer` 30→62, `track_activation_threshold` 0.70→0.31, `q_scale` 0.01→0.0025, `velocity_decay` 0.95→0.82. +- **oru_threshold 2 → 5** (i11) — ORU reserved for longer occlusions (≥5 frames); shorter gaps handled by velocity decay alone, reducing noisy replays on brief miss frames. +- **2nd calibration wave** (i12) — Optuna re-run after i11 guard. Kalman tightened ~10× (smaller `q_scale`/`r_scale`/`p_scale`), `oru_threshold` 5→14, `stage2_iou_threshold` 0.05→0.233, `velocity_decay` 0.817→0.774. +- **3rd calibration wave** (i16) — 1000-trial joint search over all 19 params after 3 new knobs added in i13–i15 (`conf_cost_weight`, `stage2_min_updates`, `giou_blend`). Key shifts: `oru_threshold` 14→0 (ORU disabled), Kalman loosened ~14×, `high_conf_det_threshold` 0.61→0.80, `stage2_min_updates` activated at 5, `giou_blend` 0.396, `conf_cost_weight` 0.170. Note: i13–i15 added these three features at `default=0` (disabled); no HOTA change at default params until i16 activated them jointly. +- **stage2_min_updates search cap fix** (i17) — search space was [0, 5]; Optuna reported 5 as optimal (hitting the cap). Manual scan revealed true peak at 12 with a cliff at 14+. Cap widened to [0, 15]. +- **Micro-calibration wave 1** (i18) — `max_interpolation_gap` 45→48, `giou_blend` 0.396→0.42, `velocity_decay` 0.827→0.82. Each sub-threshold alone (+0.02–0.03%); jointly +0.12%. +- **Micro-calibration wave 2** (i19) — `min_iou_threshold` 0.1545→0.146, `p_scale` 1.756→2.5, `q_scale` 0.002819→0.003. Optuna continuous sampler undershot near integer/round discontinuities; targeted local scan recovered the gap. + +
+ +#### Code features added + +All six features are in `trackers/core/bytetrack/tracker.py` and wired through `optimize_tracking.py`: + +| Parameter | Default | What it does | +| ---------------------- | ------- | ----------------------------------------------------------------------------------- | +| `stage2_iou_threshold` | 0.2999 | Independent IoU gate for stage-2 low-confidence detection recovery | +| `iou_age_weight` | 0.07197 | Age discount factor for stage-1 Hungarian assignment cost | +| `conf_cost_weight` | 0.1696 | Confidence boost multiplier in stage-1 assignment, biases toward certain detections | +| `stage2_min_updates` | 12 | Minimum track age (successful updates) required to enter stage-2 | +| `giou_blend` | 0.42 | Blend weight between standard IoU and GIoU in stage-1 cost matrix | +| `oru_threshold` | 0 | Minimum occlusion length (frames) to trigger ORU velocity replay | + +#### What failed (reverted) + +xcycsr 7D Kalman state (−1.2%), anisotropic Q matrix (−0.5%), EMA position blend (−0.2%), CMC detection-centroid (−27%), conf-based R scaling (−0.6%), NMS pre-filter (neutral), cascade matching (−1.2%), birth suppression (neutral), split velocity decay (−0.6%), adaptive confirmation (−0.2%), q_miss_start (neutral). + +#### Key lesson + +**Calibration waves account for ~87% of the total gain**: the three Optuna passes (i10 +1.13%, i12 +1.62%, i16 +0.19%) delivered +2.94 of the +3.09 total HOTA improvement. Joint optimisation over all parameters simultaneously reaches basins that sequential per-parameter tuning cannot. Algorithmic additions (ORU, stage-2 threshold, age discount, etc.) created new tunable structure that the calibration waves then exploited. + +--- + +### SORT — Phase 1 Campaign (MOT17-val SDP, 9 kept + 5 reverted) + +**Period**: 2026-04-05 → 2026-04-06 | **Baseline**: HOTA = 53.217 | **Final**: HOTA = 57.675 (+8.4%, +4.458 pts) | **Best commit**: `f873a75` + +Full iteration log: `.experiments/state/sort-phase1/experiments.jsonl`. The table below shows **kept** experiments only. + +#### Positive experiments + +| Hypothesis | Commit | HOTA delta at defaults | +| -------------------------------------------------------------------------------------- | --------- | ---------------------------- | +| Kalman covariance dynamics: velocity_decay, q_miss_alpha, p_reset_threshold | `8d66fba` | +0.98% | +| OC-SORT observation-centric velocity re-estimation (oru_threshold) | `de5704a` | +1.22% | +| DIoU replaces IoU in association matrix | `c4de2c6` | +0.17% | +| Confidence-weighted Hungarian assignment (conf_cost_weight) | `eefe13e` | enables Optuna headroom | +| IoU age discount for lost tracks (iou_age_weight) | `c094bcb` | enables Optuna headroom | +| Two-stage confidence-based association (high_conf_det_threshold, stage2_iou_threshold) | `e576b9e` | enables Optuna headroom | +| conf_cost_weight wiring + gap interpolation activation | `25d00c5` | activates existing feature | +| minimum_consecutive_frames 3→2 | `3555147` | faster confirmation | +| Align defaults with Optuna-tuned best_config | `ce69432` | +1.18% (single biggest jump) | + +#### Code features added + +All features are in `trackers/core/sort/tracker.py` and wired through `optimize_tracking.py`: + +| Parameter | Default | What it does | +| ------------------------- | ------- | ---------------------------------------------------------------------------------- | +| `velocity_decay` | 0.82 | Multiplicative attenuation of velocity components each missed frame | +| `q_miss_alpha` | 0.8 | Per-frame Q inflation rate for missed frames: Q_eff = Q × (1 + α × t) | +| `p_reset_threshold` | 10 | Reset error covariance P to identity on re-detection after this many missed frames | +| `oru_threshold` | 3 | Minimum occlusion length (frames) to trigger ORU virtual trajectory replay | +| `conf_cost_weight` | 0.2 | Confidence boost multiplier in Hungarian assignment cost matrix | +| `iou_age_weight` | 0.0 | Age discount factor for stale lost tracks in stage-1 solver cost | +| `high_conf_det_threshold` | 0.0 | Score gate for high-confidence first-stage detections | +| `stage2_iou_threshold` | 0.05 | Independent IoU gate for second-stage low-confidence recovery | + +#### What failed (reverted) + +xcycsr Kalman state representation (−0.51%), velocity-adaptive Q scaling (−0.06%), Mahalanobis distance gate (regression), GIoU as association metric (regression), OC-SORT velocity correction duplicate (reverted). + +#### Key lesson + +**A single calibration wave** (aligning defaults with the Optuna best, `ce69432`) accounted for +1.18% — the single largest step in the campaign. Each individual code feature added only 0.17–1.22% on its own, but together they created 8 new tunable dimensions that Optuna could jointly exploit. + +--- + +### OC-SORT — Phase 1 Campaign (MOT17-val SDP, 7 iterations + Codex co-pilot) + +**Period**: 2026-04-06 → 2026-04-07 | **Baseline**: HOTA = 53.351 | **Final**: HOTA = 58.905 (+10.4%, +5.554 pts) | **Best commit**: `573e71b` + +Full iteration log: `.experiments/state/20260406-225110/experiments.jsonl`. Team Mode (3 axes: kalman-covariance / association-pipeline / post-processing-lifecycle) + Codex co-pilot. Table shows **kept** experiments only. + +#### Positive experiments + +| Iter | Change | HOTA before → after | Δ pts | Δ % | IDSW | +| -------- | ----------------------------------------------------------- | ------------------- | ------ | ------ | ---- | +| i1 | Gap interpolation (max_interpolation_gap=20) | 53.351 → 55.361 | +2.010 | +3.77% | — | +| i2 | Kalman Q/R/P scalar multipliers (q_scale, r_scale, p_scale) | 55.361 → 55.361 | 0.0 | 0.0% | — | +| i2-codex | Codex param promotion (Optuna best → default_config) | 55.361 → 58.525 | +3.164 | +5.71% | — | +| i3 | DIoU in OCM + OCR association stages | 58.525 → 58.244 | −0.281 | −0.48% | — | +| i4 | Confidence-weighted Hungarian (conf_cost_weight) | 58.244 → 58.652 | +0.408 | +0.70% | — | +| i5 | IoU age discount for lost tracks (iou_age_weight) | 58.652 → 58.652 | 0.0 | 0.0% | — | +| i6 | P reset to identity on re-detection (p_reset_threshold) | 58.652 → 58.652 | 0.0 | 0.0% | — | +| i7 | Velocity decay + Q inflation during missed frames | 58.652 → 58.905 | +0.253 | +0.43% | 291 | + +
+Experiment descriptions + +- **Gap interpolation** (i1) — post-processing pass that linearly interpolates tracklet positions across gaps up to `max_interpolation_gap` frames. Infrastructure already existed in `optimize_tracking.py`; only `default_config.json` needed updating. +- **Kalman Q/R/P scalars** (i2) — expose global multipliers for the noise matrices to Optuna, replacing hardcoded paper defaults. No change at default params (multipliers=1); creates Optuna headroom. +- **Codex param promotion** (i2-codex) — after the i2 guard's 500-trial run found HOTA=58.525, Codex co-pilot promoted the full-precision Optuna best into `default_config.json`. Largest single step in the campaign. +- **DIoU** (i3) — replaced `sv.box_iou_batch` with `_compute_diou_matrix` in both OCM stage (`ocsort/utils.py`) and OCR stage (`tracker.py`). DIoU penalises centre distance, recovering near-miss associations. Registered as a slight regression at 1-trial defaults (−0.28 HOTA) but within guard tolerance; kept because Optuna later recovered and surpassed with tuned DIoU threshold. +- **conf_cost_weight** (i4) — confidence boost in Hungarian cost matrix: `cost *= (1 + w × conf)`. Higher-confidence detections win ties; gate check uses raw IoU. +- **iou_age_weight** (i5) — age discount for stale lost tracks in stage-1 solver: `cost *= 1/(1 + w × max(0, t−1))`. Pushes long-lost tracks to OCR recovery, keeps stage-1 for recently-seen tracks. Creates Optuna headroom. +- **p_reset_threshold** (i6) — after a gap ≥ threshold frames, reset covariance P to identity on re-detection, discarding stale accumulated uncertainty. Creates Optuna headroom. +- **velocity_decay + q_miss_alpha** (i7) — during missed frames: attenuate velocity components by `velocity_decay` and inflate Q by `(1 + alpha × t)`. Reduces prediction drift during occlusion. Optuna found velocity_decay=0.926, q_miss_alpha=0.512. + +
+ +#### Code features added + +All features are in `trackers/core/ocsort/tracker.py` + `tracklet.py` and wired through `optimize_tracking.py`: + +| Parameter | Default | What it does | +| --------------------------------- | ------- | --------------------------------------------------------------------- | +| `q_scale` / `r_scale` / `p_scale` | tuned | Global multipliers for Kalman Q, R, P noise matrices | +| `conf_cost_weight` | 0.9699 | Confidence boost in Hungarian cost; gate check uses raw IoU | +| `iou_age_weight` | 0.4279 | Age discount for stale lost tracks in stage-1 solver cost | +| `p_reset_threshold` | 8 | Reset P to identity when re-detected after ≥ this many missed frames | +| `velocity_decay` | 0.9260 | Multiplicative attenuation of velocity components each missed frame | +| `q_miss_alpha` | 0.5123 | Per-frame Q inflation rate for missed frames: Q_eff = Q × (1 + α × t) | + +#### What failed (reverted) + +Two Codex co-pilot passes hit transient guard failures (sort HOTA output missing during parallel 500-trial runs) and were reverted; neither represented a real code regression — re-runs passed. No algorithmic hypotheses were reverted. + +#### Key lesson + +**`direction_consistency_weight` is near-useless on SDP**: Optuna converged to 0.0006 (essentially zero) for the OCM direction signal. Confidence-based assignment (`conf_cost_weight` = 0.97) proved far more effective as a tiebreaker. The Codex param-promotion step (i2-codex, +5.71%) shows how much headroom existed in the pre-campaign hyperparameters — a single calibration wave after exposing Kalman scalars to Optuna captured the majority of the total gain. + +--- + +## Target analysis + +The ByteTrack Phase 2 campaign target of HOTA = 68.0 requires real architectural improvements, not parameter search — Optuna alone on FRCNN detections plateaus around 52–53. + +**HOTA formula**: HOTA = √(DetA × AssA) × 100, where DetA measures detection accuracy and AssA measures ID-consistency over time. + +**DetA ceiling from FRCNN**: bounded to ≈ 0.55–0.62 regardless of the tracker — a perfect tracker cannot recover detections the detector missed. + +**Estimated ceilings (FRCNN)**: + +| Scenario | DetA | AssA | HOTA | +| --------------------------------------- | ---- | ---- | ---- | +| Default params, current code (baseline) | 0.57 | 0.44 | 50.4 | +| Optuna only, no code changes | 0.57 | 0.55 | 56.0 | +| Code improvements + Optuna | 0.59 | 0.65 | 61.9 | +| Theoretical IoU-only ceiling | 0.62 | 0.65 | 63.5 | + +**Published reference points** (IoU-only, no ReID, FRCNN public detections; val scores ~3–5 pts above test): + +- ByteTrack — MOT17 test: HOTA ≈ 47.5; val ≈ 50–52 +- OC-SORT — MOT17 test: HOTA ≈ 52.4; val ≈ 55–57 +- BoT-SORT (no ReID) — MOT17 test: HOTA ≈ 53.1; val ≈ 56–58 + +## Hard boundaries + +See `program.md` for the full contract. Short version: + +- Metrics are computed via `trackers.eval` — no substitution +- Ground truth (`gt/gt.txt`) is never read at inference time +- Detections come from `det/det.txt` inside the sequence directory — FRCNN or generated YOLO, never oracle data +- Evolve the target tracker — do not swap it for a different algorithm mid-campaign + +## Setup + +### Dependencies + +```bash +# Run from the project root (not autotune/) +uv sync --group optimize # installs optuna[rdb] + fire + inference +``` + +### FRCNN detections (bundled — no API key needed) + +```bash +trackers download mot17 --split val --asset annotations,detections +cd autotune +uv run python optimize_tracking.py bytetrack frcnn --fast # expect HOTA ~51.2 +uv run python optimize_tracking.py sort frcnn --fast # SORT sanity check +uv run python optimize_tracking.py ocsort frcnn --fast # OC-SORT sanity check +``` + +### RF-DETR detections (no API key — weights auto-downloaded) + +```bash +cd autotune && uv run python generate_detections.py --model rfdetr/l +# Verify +uv run python optimize_tracking.py bytetrack rfdetr --fast +``` + +### Custom detections (bring your own detector) + +Any detector whose output can be formatted as MOT detection files works. Create a sibling directory for each MOT17-val sequence following the layout below: + +``` +~/.cache/trackers/mot17/val/ + MOT17-04-MYDET/ + det/det.txt ← your detections in MOT format (see below) + gt -> ../MOT17-04-FRCNN/gt ← symlink — required by the evaluator + img1 -> ../MOT17-04-FRCNN/img1 ← symlink — optional unless frames are needed +``` + +MOT detection format — one detection per line: + +``` +frame_idx,-1,x,y,w,h,confidence,-1,-1,-1 +``` + +where `(x, y)` is the top-left corner, `(w, h)` is width/height, and `id=-1` marks raw detections (not tracked identities). Then evaluate by passing the detector name as `det_source` — unknown names are uppercased automatically to form the directory tag: + +```bash +cd autotune +uv run python optimize_tracking.py bytetrack mydet +uv run python optimize_tracking.py bytetrack mydet --n-trials 50 +``` + +`mydet` → searches for `MOT17-{N}-MYDET/` directories. No extra flags needed. + +## Pre-flight checks + +Before starting the campaign loop, all steps must pass: + +| Check | Command | Expected result | +| ------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------ | +| Dependencies | `uv sync --group optimize` | Resolves without error | +| MOT17 data | `trackers download mot17 --split val --asset annotations,detections,frames` | Downloads to `~/.cache/trackers/mot17/val/` | +| RF-DETR | `uv run python generate_detections.py --model rfdetr/l` | Creates `MOT17-{N}-RFDETR/` sibling dirs; no API key or weights file | +| YOLO World | `uv run python generate_detections.py --model yolo_world/l` | Creates `MOT17-{N}-YOLOWORLD/` sibling dirs; requires `ROBOFLOW_API_KEY` | +| Metric sanity | `uv run python optimize_tracking.py bytetrack frcnn --fast` | Prints `__METRICS__: HOTA≈50–55` (FRCNN-val, single seq) | + +> **Bundled-only run** (no frames needed): use `frcnn` as det-source. Expect `HOTA≈50–55`. + +The campaign metric command uses `uv run` — bare `python` will fail with `ModuleNotFoundError: No module named 'fire'` because `fire` only lives in the `uv` virtualenv. + +## Run the agent + +### Manual loop + +```bash +claude # or any coding agent +> Read program.md and start the experiment loop. +``` + +### Run with /optimize + +If you use [Borda's Claude Code skill suite](https://github.com/Borda/.ai-home), the `/optimize` skill drives the loop directly from `program.md`: + +```bash +claude +> /optimize run autotune/program.md # uses algo/det_source from Config (defaults) +> /optimize run autotune/program.md "algo=sort" # override tracker without editing the file +> /optimize run autotune/program.md "algo=ocsort det_source=dpm" # override both +> /optimize run autotune/program.md "algo=sort" --codex --docker # with Codex co-pilot + Docker sandbox +``` + +The skill handles the full iteration loop — baseline measurement, agent-driven code changes, metric verification, auto-rollback on regression, and a final results report. `algo` and `det_source` in the Config block are **defaults**; pass them as `key=value` clarifications to override per run without editing the file. To run a tuning-only pass (Optuna, no code changes), set `agent_strategy: perf` in `program.md` before launching. See the skill docs for `--team` and `--codex` flags. + +## Files + +| File | Who edits | Purpose | +| ------------------------ | --------- | --------------------------------------------------------------------------------------------------------------------------------------------- | +| `README.md` | Human | This file | +| `program.md` | Human | Research contract — tracker-agnostic via `{algo}`/`{det_source}` placeholders; `algo`/`det_source` in Config are defaults overridable per run | +| `generate_detections.py` | Human | Detection generation for any supported model; creates `MOT17-{N}-{TAG}/` sibling directories | +| `default_config.json` | Human | Default tracker params for baseline eval — edit here, not in `optimize_tracking.py` | +| `search_space.json` | Agent | Optuna search space — add/remove params or adjust ranges here, not in the script | +| `optimize_tracking.py` | Agent | Optuna runner — positional `tracker det_source`; `--n-trials N`; `--det-tag TAG` for custom detectors | +| `best_config.json` | Agent | Best Optuna params keyed by `{tracker: {det_source: {hota, config}}}` — written after `--n-trials > 1` | + +To run a campaign for a different tracker, change `algo` in the Config block (`sort`, `bytetrack`, or `ocsort`) — or pass `"algo=sort"` as a clarification override without editing the file. `det_source` can be overridden the same way (`"algo=sort det_source=dpm"`). + +## References + +- **ByteTrack**: Zhang et al., ["ByteTrack: Multi-Object Tracking by Associating Every Detection Box"](https://arxiv.org/abs/2110.06864), ECCV 2022 · [official implementation](https://github.com/FoundationVision/ByteTrack) +- **SORT**: Bewley et al., ["Simple Online and Realtime Tracking"](https://arxiv.org/abs/1602.00763), ICIP 2016 · [official implementation](https://github.com/abewley/sort) +- **OC-SORT**: Cao et al., ["Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking"](https://arxiv.org/abs/2203.14360), CVPR 2023 · [official implementation](https://github.com/noahcao/OC_SORT) +- **HOTA**: Luiten et al., ["HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking"](https://arxiv.org/abs/2009.07736), IJCV 2021 +- **Optuna**: [optuna.org](https://optuna.org) — open-source hyperparameter optimization framework; Akiba et al., ["Optuna: A Next-generation Hyperparameter Optimization Framework"](https://arxiv.org/abs/1907.10902), KDD 2019 +- **MOT17**: Milan et al., ["MOT16: A Benchmark for Multi-Object Tracking"](https://arxiv.org/abs/1603.00831), arXiv 2016; benchmark and leaderboard at [Codabench](https://www.codabench.org/competitions/10049/) +- **[autoresearch](https://github.com/karpathy/autoresearch) pattern**: Karpathy, autonomous research loop via coding agents diff --git a/autotune/best_config.json b/autotune/best_config.json new file mode 100644 index 00000000..1675df6d --- /dev/null +++ b/autotune/best_config.json @@ -0,0 +1,264 @@ +{ + "bytetrack": { + "yolo": { + "hota": 48.58818539944531, + "config": { + "lost_track_buffer": 54, + "track_activation_threshold": 0.36662081625497334, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.14829028655285478, + "high_conf_det_threshold": 0.40316579393568125, + "q_scale": 0.0016120031702703274, + "r_scale": 0.03117576609478459, + "p_scale": 1.1880636938315343, + "velocity_decay": 0.9985304615585172, + "q_miss_alpha": 0.27608671295409976, + "max_interpolation_gap": 4, + "p_reset_threshold": 1 + } + }, + "frcnn": { + "hota": 51.84881143584812, + "config": { + "lost_track_buffer": 43, + "track_activation_threshold": 0.8998461360575083, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.24558122495848753, + "high_conf_det_threshold": 0.6667091892736219, + "q_scale": 0.025972652071928594, + "r_scale": 0.6376872626815574, + "p_scale": 8.248991281945695, + "velocity_decay": 0.8526710714527408, + "q_miss_alpha": 0.26730473472924804, + "max_interpolation_gap": 30, + "p_reset_threshold": 14 + } + }, + "sdp": { + "hota": 59.13051418925587, + "config": { + "lost_track_buffer": 51, + "track_activation_threshold": 0.5114905649202629, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.052265500917315036, + "stage2_iou_threshold": 0.29979835373383396, + "iou_age_weight": 0.05005507049974921, + "high_conf_det_threshold": 0.6487131729567451, + "q_scale": 0.0017128495311350704, + "r_scale": 0.9309506015595449, + "p_scale": 2.902648663733205, + "velocity_decay": 0.964229114603559, + "q_miss_alpha": 0.8418166381682424, + "max_interpolation_gap": 45, + "p_reset_threshold": 26, + "oru_threshold": 13, + "conf_cost_weight": 0.06609412915120773, + "stage2_min_updates": 13, + "giou_blend": 0.28382437286975765 + } + }, + "yoloworld": { + "hota": 43.11003498903179, + "config": { + "lost_track_buffer": 78, + "track_activation_threshold": 0.3007488167595766, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.29600024386428847, + "high_conf_det_threshold": 0.31512153966857215, + "q_scale": 0.0011611836655743986, + "r_scale": 0.05875171859825888, + "p_scale": 3.4139970087714717, + "velocity_decay": 0.880087523345162, + "q_miss_alpha": 0.1891821209703391, + "max_interpolation_gap": 29, + "p_reset_threshold": 3 + } + }, + "rfdetr": { + "hota": 45.08010773156588, + "config": { + "lost_track_buffer": 23, + "track_activation_threshold": 0.3745944278462327, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.27457871879161927, + "high_conf_det_threshold": 0.34417413371181516, + "q_scale": 0.0015034333915341493, + "r_scale": 0.030618921917120717, + "p_scale": 1.7599576140653497, + "velocity_decay": 0.9900231907191813, + "q_miss_alpha": 0.1359643548005466, + "max_interpolation_gap": 0, + "p_reset_threshold": 3 + } + }, + "dpm": { + "hota": 33.46778161150883, + "config": { + "lost_track_buffer": 62, + "track_activation_threshold": 0.4016241731191689, + "minimum_consecutive_frames": 3, + "minimum_iou_threshold": 0.2578024340169574, + "high_conf_det_threshold": 0.31783726371324145, + "q_scale": 0.01271390214592417, + "r_scale": 0.7726328015050697, + "p_scale": 0.5090347271489747, + "velocity_decay": 0.9908775501200271, + "q_miss_alpha": 0.03390487246647904, + "max_interpolation_gap": 22, + "p_reset_threshold": 14 + } + } + }, + "sort": { + "yolo": { + "hota": 48.96250176039076, + "config": { + "lost_track_buffer": 32, + "track_activation_threshold": 0.13745663808577574, + "minimum_consecutive_frames": 3, + "minimum_iou_threshold": 0.1246551542543195, + "max_interpolation_gap": 6 + } + }, + "frcnn": { + "hota": 51.48837202767612, + "config": { + "lost_track_buffer": 51, + "track_activation_threshold": 0.8999954169593144, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.13146703885810584, + "max_interpolation_gap": 30 + } + }, + "sdp": { + "hota": 58.02555700576912, + "config": { + "lost_track_buffer": 95, + "track_activation_threshold": 0.08514177018576655, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.2647618093617279, + "max_interpolation_gap": 56, + "velocity_decay": 0.7133017576736629, + "q_miss_alpha": 1.2801764098034227, + "p_reset_threshold": 28, + "oru_threshold": 10, + "conf_cost_weight": 0.2450541948445727, + "iou_age_weight": 0.10759441095050457, + "high_conf_det_threshold": 0.6620881582466704, + "stage2_iou_threshold": 0.27743640261484814 + } + }, + "yoloworld": { + "hota": 45.62872918360868, + "config": { + "lost_track_buffer": 57, + "track_activation_threshold": 0.16444914562248444, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.2095965537406632, + "max_interpolation_gap": 30 + } + }, + "rfdetr": { + "hota": 44.50231724515753, + "config": { + "lost_track_buffer": 12, + "track_activation_threshold": 0.31939618886129845, + "minimum_consecutive_frames": 4, + "minimum_iou_threshold": 0.5589129313391519, + "max_interpolation_gap": 53 + } + }, + "dpm": { + "hota": 31.961614517039433, + "config": { + "lost_track_buffer": 26, + "track_activation_threshold": 0.3952414540018741, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.3298630715178816, + "max_interpolation_gap": 22 + } + } + }, + "ocsort": { + "yolo": { + "hota": 48.99644225788888, + "config": { + "lost_track_buffer": 41, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.09880017257768466, + "direction_consistency_weight": 0.14863194608182262, + "high_conf_det_threshold": 0.3004488138251855, + "delta_t": 4, + "max_interpolation_gap": 30 + } + }, + "frcnn": { + "hota": 52.21799284058125, + "config": { + "lost_track_buffer": 60, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.19877407987839066, + "direction_consistency_weight": 0.3781799818765092, + "high_conf_det_threshold": 0.30884266952034833, + "delta_t": 4, + "max_interpolation_gap": 29 + } + }, + "sdp": { + "hota": 58.9053700075999, + "config": { + "lost_track_buffer": 74, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.1487990736044123, + "direction_consistency_weight": 0.0006177923357997676, + "high_conf_det_threshold": 0.6875834299207869, + "delta_t": 1, + "max_interpolation_gap": 42, + "q_scale": 0.7203257524174236, + "r_scale": 1.1889118447922835, + "p_scale": 0.09520352672425254, + "conf_cost_weight": 0.9698770881869533, + "iou_age_weight": 0.42792379789381346, + "p_reset_threshold": 8, + "velocity_decay": 0.9259939633989623, + "q_miss_alpha": 0.5122722647370503 + } + }, + "yoloworld": { + "hota": 41.40629130717004, + "config": { + "lost_track_buffer": 63, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.2836638034433728, + "direction_consistency_weight": 0.46407951150231586, + "high_conf_det_threshold": 0.31611303531562523, + "delta_t": 2, + "max_interpolation_gap": 24 + } + }, + "rfdetr": { + "hota": 47.31064661267384, + "config": { + "lost_track_buffer": 40, + "minimum_consecutive_frames": 4, + "minimum_iou_threshold": 0.24666708190317915, + "direction_consistency_weight": 0.30169607868951015, + "high_conf_det_threshold": 0.3064234735815301, + "delta_t": 1, + "max_interpolation_gap": 29 + } + }, + "dpm": { + "hota": 35.23836085354372, + "config": { + "lost_track_buffer": 58, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.18442352572537662, + "direction_consistency_weight": 0.037352813237567214, + "high_conf_det_threshold": 0.3333717714047363, + "delta_t": 4, + "max_interpolation_gap": 29 + } + } + } +} diff --git a/autotune/default_config.json b/autotune/default_config.json new file mode 100644 index 00000000..bedc3a05 --- /dev/null +++ b/autotune/default_config.json @@ -0,0 +1,56 @@ +{ + "_comment": "Default tracker parameters used for baseline evaluation (--n-trials 1).", + "_edit_here": "Edit this file to change defaults. Do not hard-code values in optimize_tracking.py.", + "bytetrack": { + "lost_track_buffer": 57, + "track_activation_threshold": 0.3328, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.146, + "stage2_iou_threshold": 0.2999, + "iou_age_weight": 0.07197, + "high_conf_det_threshold": 0.7952, + "q_scale": 0.003, + "r_scale": 0.5762, + "p_scale": 2.5, + "velocity_decay": 0.82, + "q_miss_alpha": 0.7961, + "max_interpolation_gap": 48, + "p_reset_threshold": 12, + "oru_threshold": 0, + "conf_cost_weight": 0.1696, + "stage2_min_updates": 12, + "giou_blend": 0.42 + }, + "sort": { + "lost_track_buffer": 26, + "track_activation_threshold": 0.9725, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.275, + "max_interpolation_gap": 57, + "velocity_decay": 0.82, + "q_miss_alpha": 0.8, + "p_reset_threshold": 10, + "oru_threshold": 3, + "conf_cost_weight": 0.2, + "iou_age_weight": 0.0, + "high_conf_det_threshold": 0.0, + "stage2_iou_threshold": 0.05 + }, + "ocsort": { + "lost_track_buffer": 74, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.1487990736044123, + "direction_consistency_weight": 0.0006177923357997676, + "high_conf_det_threshold": 0.6875834299207869, + "delta_t": 1, + "max_interpolation_gap": 42, + "q_scale": 0.7203257524174236, + "r_scale": 1.1889118447922835, + "p_scale": 0.09520352672425254, + "conf_cost_weight": 0.9698770881869533, + "iou_age_weight": 0.42792379789381346, + "p_reset_threshold": 8, + "velocity_decay": 0.9259939633989623, + "q_miss_alpha": 0.5122722647370503 + } +} diff --git a/autotune/generate_codabench.py b/autotune/generate_codabench.py new file mode 100644 index 00000000..368f2db3 --- /dev/null +++ b/autotune/generate_codabench.py @@ -0,0 +1,377 @@ +#!/usr/bin/env python3 +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""generate_codabench.py — Generate a MOT17 CodaBench submission ZIP. + +Runs a tracker over the MOT17 test sequences and packages the results into +a flat ZIP ready for upload to https://www.codabench.org/competitions/10049. + +Submission format (21 files, flat ZIP): + MOT17-{01,03,06,07,08,12,14}-{DPM,FRCNN,SDP}.txt + +Each line (10 comma-separated values, 1-based frame and id): + frame, id, bb_left, bb_top, bb_width, bb_height, conf, x, y, z + where x/y/z are always -1 (2-D challenge). + +Detector variants +----------------- +``--det-source`` selects which detection file(s) to use: + +* ``frcnn`` / ``dpm`` / ``sdp`` — one of the three public MOT17 bundled + detectors. The tracker runs on that detector's detections; the same + result is written to all three detector-name slots in the ZIP (MOT17 + requires 21 files regardless of which detector was used). + +* ``all`` — runs the tracker separately on each of DPM, FRCNN, and SDP; + produces 21 distinct result files. + +* Any other string (e.g. ``rfdetr``) — treats it as a custom detector tag. + Expects ``{dataset_dir}/MOT17-{seq}-{TAG}/det/det.txt`` (TAG = uppercased + det-source string). Result is copied to all three DET slots in the ZIP. + +Config loading +-------------- +Tracker params are resolved in this order (highest priority first): + +1. CLI keyword arguments (``--lost-track-buffer 30``, etc.) +2. ``best_config.json`` entry for ``tracker / det_source`` (if the file exists) +3. Tracker constructor defaults + +Usage: + # Best config for bytetrack+frcnn, result replicated to all 3 det slots: + uv run python generate_codabench.py bytetrack --det-source frcnn + + # Run all 3 public detectors separately (21 unique result files): + uv run python generate_codabench.py bytetrack --det-source all + + # Custom RF-DETR detections, bytetrack, explicit params: + uv run python generate_codabench.py bytetrack --det-source rfdetr \\ + --lost-track-buffer 50 --track-activation-threshold 0.4 + + # OC-SORT with SDP detections: + uv run python generate_codabench.py ocsort --det-source sdp + +Prerequisites: + trackers download mot17 --split test --asset detections + (frames are NOT needed for detection-based tracking) +""" + +from __future__ import annotations + +import sys +import zipfile +from pathlib import Path +from typing import Any + +import numpy as np +import supervision as sv +from rich.console import Console +from rich.progress import BarColumn, MofNCompleteColumn, Progress, TextColumn + +console = Console() +_err = Console(stderr=True) + +# --------------------------------------------------------------------------- +# Constants +# --------------------------------------------------------------------------- + +_TEST_SEQ_IDS = ["01", "03", "06", "07", "08", "12", "14"] +_PUBLIC_DET_TAGS = ["DPM", "FRCNN", "SDP"] +_DEFAULT_DATASET_DIR = Path(__file__).parent / "mot17" / "test" + +# --------------------------------------------------------------------------- +# Tracker construction +# --------------------------------------------------------------------------- + +# Global to support Kalman monkey-patch for ByteTrack +_ORIG_KALMAN_INIT: Any = None + + +def _apply_kalman_patch(params: dict, tracker_name: str) -> None: + """Monkey-patch ByteTrack Kalman matrices from ``params``. No-op for others.""" + if tracker_name != "bytetrack": + return + + global _ORIG_KALMAN_INIT + from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker + + if _ORIG_KALMAN_INIT is None: + _ORIG_KALMAN_INIT = ByteTrackKalmanBoxTracker._initialize_kalman_filter + + q = params.get("q_scale", 0.01) + r = params.get("r_scale", 0.1) + p = params.get("p_scale", 1.0) + vel_decay = params.get("velocity_decay", 0.95) + q_miss = params.get("q_miss_alpha", 0.1) + orig = _ORIG_KALMAN_INIT + + def _patched(self: ByteTrackKalmanBoxTracker) -> None: + orig(self) + self.Q = np.eye(self.Q.shape[0], dtype=np.float32) * q + self.R = np.eye(self.R.shape[0], dtype=np.float32) * r + self.P = np.eye(self.P.shape[0], dtype=np.float32) * p + + setattr(ByteTrackKalmanBoxTracker, "_initialize_kalman_filter", _patched) + setattr(ByteTrackKalmanBoxTracker, "velocity_decay", vel_decay) + setattr(ByteTrackKalmanBoxTracker, "q_miss_alpha", q_miss) + setattr( + ByteTrackKalmanBoxTracker, + "p_reset_threshold", + params.get("p_reset_threshold", 5), + ) + setattr(ByteTrackKalmanBoxTracker, "oru_threshold", params.get("oru_threshold", 2)) + + +def _build_tracker(params: dict, tracker_name: str): + """Construct a tracker from ``params``.""" + _apply_kalman_patch(params, tracker_name) + + if tracker_name == "bytetrack": + from trackers import ByteTrackTracker + + return ByteTrackTracker( + lost_track_buffer=params.get("lost_track_buffer", 30), + minimum_consecutive_frames=params.get("minimum_consecutive_frames", 1), + minimum_iou_threshold=params.get("minimum_iou_threshold", 0.2), + track_activation_threshold=params.get("track_activation_threshold", 0.25), + high_conf_det_threshold=params.get("high_conf_det_threshold", 0.6), + ) + if tracker_name == "sort": + from trackers import SORTTracker + + return SORTTracker( + lost_track_buffer=params.get("lost_track_buffer", 1), + minimum_consecutive_frames=params.get("minimum_consecutive_frames", 3), + minimum_iou_threshold=params.get("minimum_iou_threshold", 0.3), + ) + if tracker_name == "ocsort": + from trackers import OCSORTTracker + + return OCSORTTracker( + lost_track_buffer=params.get("lost_track_buffer", 30), + minimum_consecutive_frames=params.get("minimum_consecutive_frames", 3), + minimum_iou_threshold=params.get("minimum_iou_threshold", 0.3), + direction_consistency_weight=params.get( + "direction_consistency_weight", 0.5 + ), + high_conf_det_threshold=params.get("high_conf_det_threshold", 0.6), + delta_t=params.get("delta_t", 3), + ) + raise ValueError( + f"Unknown tracker: {tracker_name!r}. Choose: sort | bytetrack | ocsort" + ) + + +# --------------------------------------------------------------------------- +# Sequence tracking +# --------------------------------------------------------------------------- + + +def _run_sequence(tracker, det_file: Path, max_interpolation_gap: int = 0) -> list[str]: + """Run tracker on one sequence's detections; return MOT-format lines. + + Args: + tracker: Initialised tracker instance (will be reset before use). + det_file: Path to the MOT-format detection file. + max_interpolation_gap: Fill gaps up to this many frames (0 = off). + + Returns: + List of formatted MOT result lines (no trailing newline per line). + """ + from trackers.io.mot import _load_mot_file, _mot_frame_to_detections + + tracker.reset() + detections_data = _load_mot_file(det_file) + if not detections_data: + return [] + + max_frame = max(detections_data.keys()) + lines: list[str] = [] + + for frame_idx in range(1, max_frame + 1): + dets = ( + _mot_frame_to_detections(detections_data[frame_idx]) + if frame_idx in detections_data + else sv.Detections.empty() + ) + tracked = tracker.update(dets) + + if tracked.tracker_id is None: + continue + + for i, tid in enumerate(tracked.tracker_id): + if tid < 0: + continue + x1, y1, x2, y2 = tracked.xyxy[i] + w, h = x2 - x1, y2 - y1 + conf = ( + float(tracked.confidence[i]) if tracked.confidence is not None else 1.0 + ) + # MOT format uses 1-based track IDs; tracker returns 0-based + lines.append( + f"{frame_idx},{int(tid) + 1},{x1:.2f},{y1:.2f}," + f"{w:.2f},{h:.2f},{conf:.4f},-1,-1,-1" + ) + + if max_interpolation_gap > 0: + from trackers.core.sort.utils import interpolate_mot_gaps + + lines = interpolate_mot_gaps(lines, max_gap=max_interpolation_gap) + + return lines + + +# --------------------------------------------------------------------------- +# Main entry point +# --------------------------------------------------------------------------- + + +def generate_submission( + tracker: str = "bytetrack", + det_source: str = "all", + dataset_dir: str | None = None, + output: str | None = None, + **kwargs: Any, +) -> None: + """Generate a MOT17 CodaBench submission ZIP. + + Args: + tracker: Tracker algorithm — ``sort``, ``bytetrack``, or ``ocsort``. + det_source: Detection source tag. One of ``dpm``, ``frcnn``, ``sdp``, + ``all`` (run all three public detectors separately), or a custom tag + (e.g. ``rfdetr``) pointing to + ``{dataset_dir}/MOT17-{seq}-{TAG}/det/det.txt``. + dataset_dir: Path to the MOT17 test directory. + Defaults to the ``mot17/test`` sibling of this script. + output: Path for the output ZIP file. + Defaults to ``submission-{tracker}-{det_source}.zip``. + **kwargs: Tracker parameter overrides passed directly to the tracker + constructor (e.g. ``lost_track_buffer=50``). + + Examples: + Run from the autotune directory: + + >>> # uv run python generate_codabench.py bytetrack + >>> # uv run python generate_codabench.py bytetrack --det-source all + """ + data_dir = Path(dataset_dir) if dataset_dir else _DEFAULT_DATASET_DIR + det_source_norm = det_source.lower() + + if output is None: + output = f"submission-{tracker}-{det_source_norm}.zip" + output_path = Path(output) + + if det_source_norm == "all": + run_pairs: list[tuple[str, str]] = [(t, t) for t in _PUBLIC_DET_TAGS] + else: + file_tag = det_source_norm.upper() + run_pairs = [(file_tag, file_tag)] + + params = dict(kwargs) + max_interp = int(params.pop("max_interpolation_gap", 0)) + + # Report configuration + console.print("\n[bold]MOT17 submission generator[/bold]") + console.print(f" tracker : [cyan]{tracker}[/cyan]") + console.print(f" det_source: [cyan]{det_source}[/cyan]") + console.print(f" dataset : [cyan]{data_dir}[/cyan]") + console.print(f" output : [cyan]{output_path}[/cyan]") + if params: + console.print(f" params : {params}") + console.print() + + # Validate dataset dir + if not data_dir.exists(): + _err.print(f"[red]Error: dataset_dir does not exist: {data_dir}[/red]") + _err.print(" Download test data first:") + _err.print(" trackers download mot17 --split test --asset detections") + sys.exit(1) + + # Collect (output_filename → lines) mapping + # Key = "MOT17-{seq_id}-{DET_TAG}.txt" + results: dict[str, list[str]] = {} + + total_jobs = len(_TEST_SEQ_IDS) * len(run_pairs) + + with Progress( + TextColumn(" {task.description}"), + BarColumn(), + MofNCompleteColumn(), + console=console, + ) as progress: + task = progress.add_task("Tracking", total=total_jobs) + + for seq_id in _TEST_SEQ_IDS: + for out_tag, file_tag in run_pairs: + seq_dir_name = f"MOT17-{seq_id}-{file_tag}" + det_file = data_dir / seq_dir_name / "det" / "det.txt" + + progress.update(task, description=f"[cyan]{seq_dir_name}[/cyan]") + + if not det_file.exists(): + _err.print( + f"\n[yellow]Warning: detection file not found, " + f"skipping: {det_file}[/yellow]" + ) + progress.advance(task) + continue + + tracker_inst = _build_tracker(params, tracker) + lines = _run_sequence(tracker_inst, det_file, max_interp) + out_name = f"MOT17-{seq_id}-{out_tag}.txt" + results[out_name] = lines + progress.advance(task) + + if not results: + _err.print("[red]Error: no sequences were processed.[/red]") + sys.exit(1) + + # When using a single (non-all) source, replicate to all 3 DET slots + if det_source_norm != "all": + expanded: dict[str, list[str]] = {} + for seq_id in _TEST_SEQ_IDS: + for out_tag in _PUBLIC_DET_TAGS: + out_name = f"MOT17-{seq_id}-{out_tag}.txt" + # Find the result we produced for this sequence + src_name = next( + (k for k in results if k.startswith(f"MOT17-{seq_id}-")), + None, + ) + if src_name is not None: + expanded[out_name] = results[src_name] + results = expanded + + # Package into ZIP + output_path.parent.mkdir(parents=True, exist_ok=True) + with zipfile.ZipFile(output_path, "w", zipfile.ZIP_DEFLATED) as zf: + for filename, lines in sorted(results.items()): + content = "\n".join(lines) + ("\n" if lines else "") + zf.writestr(filename, content) + + n_files = len(results) + n_missing = 21 - n_files + console.print(f"\n[green]✓ Wrote {n_files}/21 files → {output_path}[/green]") + if n_missing > 0: + console.print( + f"[yellow] ⚠ {n_missing} files missing — upload may be rejected.[/yellow]" + ) + console.print( + f" Check that all 7 sequence directories are present in {data_dir}" + ) + else: + console.print(" Upload at: https://www.codabench.org/competitions/10049") + console.print() + + +# --------------------------------------------------------------------------- +# CLI entry point +# --------------------------------------------------------------------------- + +if __name__ == "__main__": + import fire + + fire.Fire(generate_submission) diff --git a/autotune/generate_detections.py b/autotune/generate_detections.py new file mode 100644 index 00000000..6d30ecf9 --- /dev/null +++ b/autotune/generate_detections.py @@ -0,0 +1,522 @@ +#!/usr/bin/env python3 +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""generate_detections.py — Generate detections for MOT17-val sequences. + +Runs detection inference on each frame and saves predictions in MOT format. +Each detector gets its own sequence-level sibling directory, making the detector +visible directly in the filesystem path. + +Usage: + uv run python generate_detections.py --model rfdetr/l # → RFDETR/ + uv run python generate_detections.py --model yolo_world/l # → YOLOWORLD/ + uv run python generate_detections.py --model yolo_world/l \\ + --api-key YOUR_ROBOFLOW_KEY # explicit key + uv run python generate_detections.py --seq MOT17-04 # single sequence + +Prerequisites: + 1. Install optimize group: + uv sync --group optimize + 2. Download frame images (≈ 4 GB): + trackers download mot17 --split val --asset annotations,detections,frames + +Output: + {data_dir}/{sequence_base}-{TAG}/det/det.txt (MOT-format; one file per seq) + {data_dir}/{sequence_base}-{TAG}/gt → symlink to ../{sequence_base}-FRCNN/gt + {data_dir}/{sequence_base}-{TAG}/img1 → symlink to ../{sequence_base}-FRCNN/img1 + + TAG is auto-derived from the model name (rfdetr → RFDETR, + yolo_world/l → YOLOWORLD) and can be overridden with ``--detector-tag``. + + Each line: frame_idx,-1,x,y,w,h,confidence,-1,-1,-1 + where (x, y) is the top-left corner and (w, h) is width/height. + id=-1 because these are raw detections, not tracked identities. + +Detector backends: + + rfdetr (default — no API key needed): + Model: ``rfdetr/l`` (large) — size suffix is required + Backend: native ``rfdetr`` package (>= 1.6) + Weights: downloaded automatically on first use + + yolo_world (open-vocabulary person detector): + Model: ``yolo_world/s``, ``yolo_world/m``, ``yolo_world/l`` (default), + ``yolo_world/x`` + Backend: ``inference-models`` package (>= 0.19.0) + API key: set ``ROBOFLOW_API_KEY`` env var, or pass ``--api-key`` + (needed to download weights on first use) + Text: searches for ``"person"`` via CLIP text embeddings + Outputs are person-only (single text class). + +Notes: + - Each detector writes to its own sequence-level directory (e.g. + ``MOT17-04-RFDETR/``, ``MOT17-04-YOLOWORLD/``), keeping detector outputs + fully transparent in the filesystem. Use ``--detector-tag`` to override. + - Overwrites existing det.txt if present (use --skip-existing). + - Ground truth (gt/) is never read or written; inference is detection-only. + - Sequences without an img1/ directory are skipped with a warning. +""" + +from __future__ import annotations + +from pathlib import Path +from typing import Any + +import fire +import supervision as sv +from rich.console import Console +from rich.progress import track + +console = Console() + +# --------------------------------------------------------------------------- +# Defaults +# --------------------------------------------------------------------------- + +_DEFAULT_MODEL_ID = "rfdetr/l" +_DEFAULT_CONFIDENCE = 0.1 # low threshold — let the tracker filter aggressively + +# rfdetr native (>= 1.6) uses 1-indexed COCO category IDs; person = 1 +_RFDETR_PERSON_CLASS_ID = 1 + +_RFDETR_NATIVE_MODELS = frozenset({"rfdetr/n", "rfdetr/s", "rfdetr/m", "rfdetr/l"}) +# YOLO-World with text=["person"] outputs person-only (single text class) +_YOLO_WORLD_MODELS = frozenset( + {"yolo_world/s", "yolo_world/m", "yolo_world/l", "yolo_world/x"} +) + + +# --------------------------------------------------------------------------- +# Helper: data directory + detector tag +# --------------------------------------------------------------------------- + + +def _find_data_dir() -> Path: + """Locate the MOT17-val root directory, same logic as optimize_tracking.py.""" + for candidate in [ + Path("./mot17/val"), + Path("./data/mot17/val"), + Path.home() / ".cache/trackers/mot17/val", + ]: + if candidate.exists() and any(candidate.glob("*/gt/gt.txt")): + return candidate + raise FileNotFoundError( + "MOT17 val data not found. Run:\n" + " trackers download mot17 --split val --asset annotations,detections,frames" + ) + + +def _derive_detector_tag(model: str) -> str: + """Derive an uppercase detector tag from a model identifier. + + The tag is used as the directory suffix, e.g. ``MOT17-04-RFDETR/``. + + Args: + model: Model name or Roboflow model ID, e.g. ``"rfdetr/l"``, + ``"yolo_world/x"``. + + Returns: + Short uppercase tag, e.g. ``"RFDETR"``, ``"YOLOWORLD"``. + + Examples: + RF-DETR maps to ``"RFDETR"``:: + + tag = _derive_detector_tag("rfdetr/l") + # "RFDETR" + """ + m = model.lower() + if m.startswith("yolo_world") or m.startswith("yolo-world"): + return "YOLOWORLD" + if m.startswith("rfdetr") or m.startswith("rf-detr"): + return "RFDETR" + if m.startswith("yolo"): + return "YOLO" + # Generic fallback: first hyphen-separated component, uppercased + return model.split("-")[0].upper() + + +# --------------------------------------------------------------------------- +# YOLO-World backend (inference-models >= 0.19.0) +# --------------------------------------------------------------------------- + + +def _load_yolo_world_model(model_id: str, api_key: str | None) -> Any: + """Load a YOLO-World model via the inference-models package. + + Weights are downloaded from Roboflow on first use. Set + ``ROBOFLOW_API_KEY`` or pass ``api_key`` explicitly. + + Args: + model_id: One of ``"yolo_world/s"``, ``"yolo_world/m"``, + ``"yolo_world/l"`` (default), ``"yolo_world/x"``. + api_key: Roboflow API key. Falls back to the ``ROBOFLOW_API_KEY`` + environment variable when ``None``. + + Returns: + A ``YOLOWorld`` model instance ready for ``.infer()`` calls. + + Raises: + RuntimeError: If the ``inference-models`` package is not installed. + + Examples: + Load the large YOLO-World model:: + + model = _load_yolo_world_model("yolo_world/l", api_key=None) + """ + try: + from inference.models.yolo_world import YOLOWorld + except ImportError as exc: + raise RuntimeError( + "YOLO-World backend requires the 'inference-models' package.\n" + "Install: uv sync --group optimize" + ) from exc + return YOLOWorld(model_id=model_id, api_key=api_key) + + +def _process_frame_yolo_world( + frame_path: Path, + model: Any, + confidence: float, +) -> list[str]: + """Run YOLO-World inference on one frame; return MOT-format detection lines. + + The model is queried with text prompt ``["person"]``. Predictions are + returned in center-format (x_center, y_center, width, height) and are + converted to top-left corner format for MOT output. + + Args: + frame_path: Image file whose stem is a zero-padded integer frame index. + model: A loaded ``YOLOWorld`` instance. + confidence: Minimum detection confidence threshold. + + Returns: + List of strings in MOT format: ``"frame,-1,x,y,w,h,conf,-1,-1,-1"``. + + Examples: + Output is a list of comma-separated detection strings:: + + lines = _process_frame_yolo_world(frame_path, model, confidence=0.1) + # ["1,-1,120.50,200.30,60.00,150.00,0.9200,-1,-1,-1", ...] + """ + frame_idx = int(frame_path.stem) + response = model.infer( + image=str(frame_path), + text=["person"], + confidence=confidence, + ) + lines = [] + for pred in response.predictions: + # inference returns center-format; convert to top-left for MOT + x_tl = pred.x - pred.width / 2 + y_tl = pred.y - pred.height / 2 + lines.append( + f"{frame_idx},-1,{x_tl:.2f},{y_tl:.2f}" + f",{pred.width:.2f},{pred.height:.2f},{pred.confidence:.4f},-1,-1,-1" + ) + return lines + + +# --------------------------------------------------------------------------- +# RF-DETR native backend (rfdetr >= 1.6 — no API key needed) +# --------------------------------------------------------------------------- + + +def _load_rfdetr_model(model_name: str) -> Any: + """Load a native RF-DETR model via the rfdetr package (>= 1.6). + + Weights are downloaded automatically on first use — no API key required. + + Args: + model_name: ``"rfdetr/l"`` (large). A size suffix is required; + bare ``"rfdetr"`` is not accepted. + + Returns: + An RF-DETR model instance with a ``predict(image, threshold)`` method + that returns ``sv.Detections`` directly. + + Raises: + RuntimeError: If the ``rfdetr`` package (>= 1.6) is not installed. + ValueError: If ``model_name`` is not one of the accepted size variants. + + Examples: + Load the large RF-DETR model:: + + model = _load_rfdetr_model("rfdetr/l") + """ + _RFDETR_CLASS_MAP = { + "rfdetr/n": "RFDETRNano", + "rfdetr/s": "RFDETRSmall", + "rfdetr/m": "RFDETRMedium", + "rfdetr/l": "RFDETRLarge", + } + class_name = _RFDETR_CLASS_MAP.get(model_name) + if class_name is None: + raise ValueError( + f"Unknown RF-DETR model {model_name!r}. " + f"Supported: {', '.join(sorted(_RFDETR_CLASS_MAP))}" + ) + try: + import rfdetr as _rfdetr + + return getattr(_rfdetr, class_name)() + except ImportError as exc: + raise RuntimeError( + "RF-DETR backend requires the 'rfdetr' package (>= 1.6).\n" + "Install: pip install 'rfdetr>=1.6'\n" + "Weights are downloaded automatically on first use." + ) from exc + + +def _process_frame_rfdetr( + frame_path: Path, + model: Any, + confidence: float, +) -> list[str]: + """Run native RF-DETR inference on one frame; return MOT-format detection lines. + + Args: + frame_path: Image file whose stem is a zero-padded integer frame index. + model: A loaded RF-DETR model instance (``RFDETRLarge`` or ``RFDETRBase``). + confidence: Minimum detection confidence threshold. + + Returns: + List of strings in MOT format: ``"frame,-1,x,y,w,h,conf,-1,-1,-1"``. + + Examples: + Output is a list of comma-separated detection strings:: + + lines = _process_frame_rfdetr(frame_path, model, confidence=0.1) + # ["1,-1,120.50,200.30,60.00,150.00,0.9200,-1,-1,-1", ...] + """ + from PIL import Image + + frame_idx = int(frame_path.stem) + image = Image.open(frame_path).convert("RGB") + detections: sv.Detections = model.predict(image, threshold=confidence) + # rfdetr returns 1-indexed COCO category IDs (person = 1), not 0-indexed + detections = detections[detections.class_id == _RFDETR_PERSON_CLASS_ID] # type: ignore[assignment] + lines = [] + for i, (x1, y1, x2, y2) in enumerate(detections.xyxy): + w = x2 - x1 + h = y2 - y1 + if detections.confidence is None: + raise RuntimeError( + f"RF-DETR returned no confidence scores for frame {frame_path.stem}." + ) + conf = float(detections.confidence[i]) + lines.append( + f"{frame_idx},-1,{x1:.2f},{y1:.2f},{w:.2f},{h:.2f},{conf:.4f},-1,-1,-1" + ) + return lines + + +# --------------------------------------------------------------------------- +# Sequence runner +# --------------------------------------------------------------------------- + + +def _run_on_sequence( + seq_dir: Path, + out_dir: Path, + model: Any, + backend: str, + confidence: float, + skip_existing: bool, +) -> int: + """Run inference on one sequence; return number of frames processed. + + Args: + seq_dir: Path to the source sequence directory with frames (e.g. + ``.../MOT17-04-FRCNN``). + out_dir: Output directory for this detector (e.g. ``.../MOT17-04-YOLOWORLD``). + Detections are written to ``out_dir/det/det.txt``. + model: Loaded model instance (rfdetr or YOLO-World). + backend: Either ``"rfdetr"`` or ``"yolo_world"``. + confidence: Minimum detection confidence. + skip_existing: If True and ``out_dir/det/det.txt`` already exists, skip. + + Returns: + Number of frames processed (0 if skipped or no frames found). + + Examples: + Returns 0 for a sequence without frames:: + + n = _run_on_sequence( + Path("/missing"), Path("/out"), model, "rfdetr", 0.1, False + ) + # n == 0 + """ + img_dir = seq_dir / "img1" + output_file = out_dir / "det" / "det.txt" + + if not img_dir.exists(): + console.print( + f" [yellow]skip[/yellow] {seq_dir.name}: img1/ not found — " + "run: trackers download mot17 --split val --asset frames" + ) + return 0 + + if skip_existing and output_file.exists(): + console.print( + f" [yellow]skip[/yellow] {seq_dir.name}: {out_dir.name}/det/det.txt exists" + ) + return 0 + + frames = sorted(img_dir.glob("*.jpg")) or sorted(img_dir.glob("*.png")) + if not frames: + console.print( + f" [yellow]skip[/yellow] {seq_dir.name}: no .jpg/.png frames in img1/" + ) + return 0 + + lines: list[str] = [] + for frame_path in track(frames, description=f" {seq_dir.name}", console=console): + if backend == "yolo_world": + lines.extend(_process_frame_yolo_world(frame_path, model, confidence)) + else: + lines.extend(_process_frame_rfdetr(frame_path, model, confidence)) + + output_file.parent.mkdir(parents=True, exist_ok=True) + output_file.write_text("\n".join(lines) + "\n" if lines else "") + return len(frames) + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +def main( + model: str = _DEFAULT_MODEL_ID, + conf: float = _DEFAULT_CONFIDENCE, + api_key: str | None = None, + detector_tag: str | None = None, + data_dir: str | None = None, + seq: str | None = None, + skip_existing: bool = False, +) -> None: + """Generate detections for MOT17-val sequences. + + Args: + model: Detector to use. RF-DETR: ``"rfdetr/n"``, ``"rfdetr/s"``, + ``"rfdetr/m"``, ``"rfdetr/l"`` (default). YOLO-World: + ``"yolo_world/s"``, ``"yolo_world/m"``, ``"yolo_world/l"``, + ``"yolo_world/x"``. + conf: Minimum detection confidence threshold (default: 0.1). + api_key: Roboflow API key for YOLO-World weight download. Falls back + to the ``ROBOFLOW_API_KEY`` environment variable when unset. + detector_tag: Uppercase tag appended to the output directory name, e.g. + ``"RFDETR"`` → ``MOT17-04-RFDETR/``. Auto-derived if unset. + data_dir: MOT17 val directory. Auto-detected if unset. + seq: Filter to a single sequence prefix, e.g. ``"MOT17-04"``. If unset, + runs on all sequences with ground-truth annotations and frames. + skip_existing: Skip sequences where ``{TAG}/det/det.txt`` already exists. + + Examples: + RF-DETR large (default):: + + uv run python generate_detections.py --model rfdetr/l + + YOLO-World large (open-vocabulary person detector):: + + uv run python generate_detections.py --model yolo_world/l + """ + is_yolo_world = model in _YOLO_WORLD_MODELS + is_rfdetr = model in _RFDETR_NATIVE_MODELS + + if not is_yolo_world and not is_rfdetr: + rfdetr_opts = ", ".join(sorted(_RFDETR_NATIVE_MODELS)) + yoloworld_opts = ", ".join(sorted(_YOLO_WORLD_MODELS)) + raise ValueError( + f"Unknown model {model!r}.\n" + f"RF-DETR options: {rfdetr_opts}\n" + f"YOLO-World options: {yoloworld_opts}" + ) + + _data_dir = Path(data_dir) if data_dir else _find_data_dir() + + # Source sequences: dirs that have actual frames (img1/) and ground truth (gt/) + # Only use dirs where img1/ is a real directory (not a symlink) — this + # excludes RFDETR/YOLOWORLD sibling dirs whose img1/ points back to FRCNN. + source_seqs = sorted( + d + for d in _data_dir.iterdir() + if d.is_dir() + and (d / "img1").is_dir() + and not (d / "img1").is_symlink() + and (d / "gt" / "gt.txt").exists() + ) + if not source_seqs: + raise FileNotFoundError( + f"No annotated sequences with frames found in {_data_dir}.\n" + "Download frames with:\n" + " trackers download mot17 --split val" + " --asset annotations,detections,frames" + ) + + if seq is not None: + source_seqs = [s for s in source_seqs if seq in s.name] + if not source_seqs: + raise ValueError(f"No sequences match filter {seq!r}") + + tag = detector_tag or _derive_detector_tag(model) + + console.print( + f"Loading model [bold]{model}[/bold] ... (detector tag: [bold]{tag}[/bold])" + ) + if is_yolo_world: + loaded_model = _load_yolo_world_model(model, api_key) + backend = "yolo_world" + else: + loaded_model = _load_rfdetr_model(model) + backend = "rfdetr" + + total_frames = 0 + for seq_dir in source_seqs: + # e.g. "MOT17-04-FRCNN" → base "MOT17-04" → out dir "MOT17-04-YOLO" + seq_base = seq_dir.name.rsplit("-", 1)[0] + out_dir = _data_dir / f"{seq_base}-{tag}" + + # Create output dir and symlinks so the directory mirrors the FRCNN structure + out_dir.mkdir(parents=True, exist_ok=True) + gt_link = out_dir / "gt" + if not gt_link.exists(): + gt_link.symlink_to(f"../{seq_dir.name}/gt") + img1_link = out_dir / "img1" + if not img1_link.exists(): + img1_link.symlink_to(f"../{seq_dir.name}/img1") + + n = _run_on_sequence( + seq_dir, + out_dir, + loaded_model, + backend, + conf, + skip_existing, + ) + total_frames += n + if n > 0: + out = out_dir / "det" / "det.txt" + n_dets = sum(1 for line in out.read_text().splitlines() if line.strip()) + console.print( + f" [green]✓[/green] {n} frames · {n_dets} dets" + f" → {out.relative_to(_data_dir)}" + ) + + console.print( + f"\n[bold green]Done.[/bold green] {total_frames} frames across" + f" {len(source_seqs)} sequence(s) → {tag}/ sibling directories." + ) + console.print( + f"Run campaign:\n" + f" [dim]cd autotune &&" + f" uv run python optimize_tracking.py bytetrack {tag.lower()}[/dim]" + ) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/autotune/guard.py b/autotune/guard.py new file mode 100644 index 00000000..250dad12 --- /dev/null +++ b/autotune/guard.py @@ -0,0 +1,44 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""Regression guard: ensures no tracker's HOTA drops >0.5% from the stored best.""" + +import json +import re +import sys +from contextlib import redirect_stdout +from io import StringIO +from pathlib import Path + +from optimize_tracking import main as optimize_tracking_main + + +def _run_search(tracker_name: str) -> str: + """Run tracker optimization and return captured stdout.""" + stdout_buffer = StringIO() + with redirect_stdout(stdout_buffer): + optimize_tracking_main(tracker=tracker_name, det_source="sdp", n_trials=500) + return stdout_buffer.getvalue() + + +best = json.loads((Path(__file__).parent / "best_config.json").read_text()) +failed = [] + +for t in ["bytetrack", "sort", "ocsort"]: + out = _run_search(t) + m = re.search(r"HOTA=([0-9.]+)", out) + if not m: + failed.append(f"{t}: no HOTA output") + continue + hota, base = float(m.group(1)), best[t]["sdp"]["hota"] + drop = (base - hota) / base * 100 + print(f"{t}: HOTA={hota:.3f} vs best={base:.3f} ({drop:+.2f}%)") + if drop > 0.5: + failed.append(f"{t}: regressed {drop:.2f}%") + +if failed: + print("GUARD FAILED:", "; ".join(failed)) + sys.exit(1) diff --git a/autotune/optimize_tracking.py b/autotune/optimize_tracking.py new file mode 100644 index 00000000..fa5830fe --- /dev/null +++ b/autotune/optimize_tracking.py @@ -0,0 +1,934 @@ +#!/usr/bin/env python3 +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""optimize_tracking.py — Optuna-based tracker search on MOT17-val. + +Usage: + python optimize_tracking.py bytetrack frcnn # ByteTrack, FRCNN dets + python optimize_tracking.py sort frcnn # SORT baseline + python optimize_tracking.py ocsort sdp # OC-SORT, SDP dets + python optimize_tracking.py bytetrack yoloworld # ByteTrack, YOLO-World dets + python optimize_tracking.py bytetrack frcnn --n-trials 200 # Full Optuna study + python optimize_tracking.py bytetrack yoloworld --n-trials 50 --fast # Quick study + python optimize_tracking.py bytetrack mydet # Custom detector + +Supported trackers: sort | bytetrack | ocsort +Supported det-sources (MOT17 bundled detectors, no setup needed): + frcnn — FRCNN bundled detections from MOT17-{N}-FRCNN/det/det.txt + sdp — SDP bundled detections from MOT17-{N}-SDP/det/det.txt + dpm — DPM bundled detections from MOT17-{N}-DPM/det/det.txt + +Supported det-sources (generated via generate_detections.py): + yoloworld — YOLO-World detections from MOT17-{N}-YOLOWORLD/det/det.txt + rfdetr — RF-DETR-L detections from MOT17-{N}-RFDETR/det/det.txt + +Custom detectors: + Pass any det_source name not in the list above (e.g. "mydet") — it is + uppercased automatically to form the directory tag (MOT17-{N}-MYDET/). + Create the sibling dirs with det/det.txt and gt/, img1/ symlinks. + +The search space and tracker construction are updated by the agent as tracker +architectures evolve — add, remove, or retune parameters to match the current code. + +Hard boundaries (never change): + - Evaluation calls go through trackers.eval — do not substitute custom metric code. + - Ground truth: always gt/gt.txt — never read at inference time. + - Detections: {seq}-{TAG}/det/det.txt only — never gt/. No ground truth at inference. + - Output format: __METRICS__ line must remain parseable by the campaign loop. +""" + +from __future__ import annotations + +import json +import math +import time +from pathlib import Path + +import fire +import numpy as np +import optuna +import supervision as sv +from rich.console import Console +from rich.progress import ( + BarColumn, + MofNCompleteColumn, + Progress, + TextColumn, + TimeRemainingColumn, +) + +optuna.logging.set_verbosity(optuna.logging.WARNING) + +_err = Console(stderr=True) + +_STUDY_NAME = "autotune" + +# Maps --det-source values to the sequence directory suffix used in the filesystem. +# generate_detections.py creates MOT17-{N}-{TAG}/ sibling dirs; FRCNN is the original. +_DET_SOURCE_TO_TAG: dict[str, str] = { + "frcnn": "FRCNN", + "sdp": "SDP", + "dpm": "DPM", + "yoloworld": "YOLOWORLD", + "rfdetr": "RFDETR", +} + +# Capture the original ByteTrack Kalman init once so repeated patching across +# trials always re-applies from the true original (not a previously patched version). +_ORIG_KALMAN_INIT = None +_ORIG_OCSORT_CREATE_FILTER = None + + +# --------------------------------------------------------------------------- +# DEFAULTS — loaded from default_config.json; edit that file, not this script +# --------------------------------------------------------------------------- + +_DEFAULTS: dict[str, dict] = { + k: v + for k, v in json.loads( + (Path(__file__).parent / "default_config.json").read_text() + ).items() + if not k.startswith("_") # strip _comment / _edit_here meta-keys +} + + +# --------------------------------------------------------------------------- +# SEARCH SPACE — loaded from search_space.json; edit that file, not this script +# --------------------------------------------------------------------------- + +_SEARCH_SPACE: dict[str, dict] = { + k: v + for k, v in json.loads( + (Path(__file__).parent / "search_space.json").read_text() + ).items() + if not k.startswith("_") # strip _comment / _edit_here / _types meta-keys +} + + +def _check_config_drift(tracker_name: str) -> None: + """Warn if default_config.json and search_space.json keys diverge for a tracker. + + When a parameter is added to one file but not the other, enqueue_trial silently + uses a random Optuna-sampled value instead of the intended default — making + ``--n-trials 1`` baselines unreliable without any error signal. + + Args: + tracker_name: Tracker identifier (e.g. ``"bytetrack"``). + """ + default_keys = set(_DEFAULTS.get(tracker_name, {}).keys()) + space_keys = set(_SEARCH_SPACE.get(tracker_name, {}).keys()) + if default_keys == space_keys: + return + parts = [] + if default_keys - space_keys: + parts.append( + f"in default_config.json but not search_space.json:" + f" {sorted(default_keys - space_keys)}" + ) + if space_keys - default_keys: + parts.append( + f"in search_space.json but not default_config.json:" + f" {sorted(space_keys - default_keys)}" + ) + _err.print( + f"[yellow]warn[/yellow] config key drift for {tracker_name!r}:" + f" {'; '.join(parts)}" + " — --n-trials 1 baseline may use random values for drifted params" + ) + + +def _define_search_space(trial: optuna.Trial, tracker_name: str) -> dict: + """Sample a parameter dict for this trial from the search_space.json definition. + + To add, remove, or retune a parameter: edit search_space.json — do not + modify this function. Supported spec keys per parameter: + type "int" | "float" | "categorical" + low / high numeric bounds (int and float) + log true → log-scale sampling (int and float; low must be >= 1 for int) + choices list of values (categorical only) + """ + space = _SEARCH_SPACE[tracker_name] + params: dict = {} + for name, spec in space.items(): + kind = spec["type"] + if kind == "int": + params[name] = trial.suggest_int( + name, spec["low"], spec["high"], log=spec.get("log", False) + ) + elif kind == "float": + params[name] = trial.suggest_float( + name, spec["low"], spec["high"], log=spec.get("log", False) + ) + elif kind == "categorical": + params[name] = trial.suggest_categorical(name, spec["choices"]) + else: + raise ValueError(f"Unknown search space type {kind!r} for param {name!r}") + return params + + +def _build_tracker(params: dict, tracker_name: str): + """Instantiate the named tracker from a parameter dict. + + Update the relevant constructor call when new parameters are added or removed. + """ + if tracker_name == "bytetrack": + from trackers import ByteTrackTracker + + return ByteTrackTracker( + lost_track_buffer=params["lost_track_buffer"], + track_activation_threshold=params["track_activation_threshold"], + minimum_consecutive_frames=params["minimum_consecutive_frames"], + minimum_iou_threshold=params["minimum_iou_threshold"], + stage2_iou_threshold=params["stage2_iou_threshold"], + iou_age_weight=params["iou_age_weight"], + high_conf_det_threshold=params["high_conf_det_threshold"], + conf_cost_weight=params.get("conf_cost_weight", 0.0), + stage2_min_updates=params.get("stage2_min_updates", 0), + giou_blend=params.get("giou_blend", 0.0), + ) + if tracker_name == "sort": + from trackers import SORTTracker + + return SORTTracker( + lost_track_buffer=params["lost_track_buffer"], + track_activation_threshold=params["track_activation_threshold"], + minimum_consecutive_frames=params["minimum_consecutive_frames"], + minimum_iou_threshold=params["minimum_iou_threshold"], + velocity_decay=params.get("velocity_decay", 0.95), + q_miss_alpha=params.get("q_miss_alpha", 0.0), + p_reset_threshold=params.get("p_reset_threshold", 0), + oru_threshold=params.get("oru_threshold", 0), + conf_cost_weight=params.get("conf_cost_weight", 0.0), + iou_age_weight=params.get("iou_age_weight", 0.0), + high_conf_det_threshold=params.get("high_conf_det_threshold", 0.0), + stage2_iou_threshold=params.get("stage2_iou_threshold", 0.05), + ) + if tracker_name == "ocsort": + from trackers import OCSORTTracker + + return OCSORTTracker( + lost_track_buffer=params["lost_track_buffer"], + minimum_consecutive_frames=params["minimum_consecutive_frames"], + minimum_iou_threshold=params["minimum_iou_threshold"], + direction_consistency_weight=params["direction_consistency_weight"], + high_conf_det_threshold=params["high_conf_det_threshold"], + delta_t=params["delta_t"], + conf_cost_weight=params.get("conf_cost_weight", 0.0), + iou_age_weight=params.get("iou_age_weight", 0.0), + p_reset_threshold=params.get("p_reset_threshold", 0), + velocity_decay=params.get("velocity_decay", 1.0), + q_miss_alpha=params.get("q_miss_alpha", 0.0), + ) + raise ValueError( + f"Unknown tracker: {tracker_name!r}. Choose: sort | bytetrack | ocsort" + ) + + +def _apply_kalman_patch(params: dict, tracker_name: str) -> None: + """Override Kalman noise matrices from params for ByteTrack and OC-SORT. + + ByteTrack: replaces Q/R/P with scaled identity matrices. + OC-SORT: multiplies the paper-default Q/R/P by global scalars, preserving + the relative noise structure while allowing Optuna to tune the overall level. + + Remove or replace this function if Kalman scales become constructor args, + or if the Kalman architecture changes to a point where simple scalar scaling + no longer makes sense. + """ + if tracker_name == "bytetrack": + global _ORIG_KALMAN_INIT + from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker + + if _ORIG_KALMAN_INIT is None: + _ORIG_KALMAN_INIT = ByteTrackKalmanBoxTracker._initialize_kalman_filter + + q = params.get("q_scale", 0.01) + r = params.get("r_scale", 0.1) + p = params.get("p_scale", 1.0) + vel_decay = params.get("velocity_decay", 0.95) + q_miss = params.get("q_miss_alpha", 0.1) + + orig = _ORIG_KALMAN_INIT + + def _patched(self: ByteTrackKalmanBoxTracker) -> None: + orig(self) + self.Q = np.eye(self.Q.shape[0], dtype=np.float32) * q + self.R = np.eye(self.R.shape[0], dtype=np.float32) * r + self.P = np.eye(self.P.shape[0], dtype=np.float32) * p + + setattr(ByteTrackKalmanBoxTracker, "_initialize_kalman_filter", _patched) + setattr(ByteTrackKalmanBoxTracker, "velocity_decay", vel_decay) + setattr(ByteTrackKalmanBoxTracker, "q_miss_alpha", q_miss) + setattr( + ByteTrackKalmanBoxTracker, + "p_reset_threshold", + params.get("p_reset_threshold", 5), + ) + setattr( + ByteTrackKalmanBoxTracker, + "oru_threshold", + params.get("oru_threshold", 2), + ) + + elif tracker_name == "ocsort": + # Multiply paper-default Q/R/P by global scalars, preserving the relative + # noise structure while allowing Optuna to tune the overall level. + global _ORIG_OCSORT_CREATE_FILTER + from trackers.utils.state_representations import XCYCSRStateEstimator + + if _ORIG_OCSORT_CREATE_FILTER is None: + _ORIG_OCSORT_CREATE_FILTER = XCYCSRStateEstimator._create_filter + + q = params.get("q_scale", 1.0) + r = params.get("r_scale", 1.0) + p = params.get("p_scale", 1.0) + orig_cf = _ORIG_OCSORT_CREATE_FILTER + + def _patched_create_filter(self: XCYCSRStateEstimator, bbox: np.ndarray): + kf = orig_cf(self, bbox) + kf.Q *= q + kf.R *= r + kf.P *= p + return kf + + setattr(XCYCSRStateEstimator, "_create_filter", _patched_create_filter) + + +# --------------------------------------------------------------------------- +# EVALUATION — do not modify (metrics + dataset integrity) +# --------------------------------------------------------------------------- + + +def _find_data_dir() -> Path: + for candidate in [ + Path("./mot17/val"), + Path("./data/mot17/val"), + Path.home() / ".cache/trackers/mot17/val", + ]: + if candidate.exists() and any(candidate.glob("*/gt/gt.txt")): + return candidate + raise FileNotFoundError( + "MOT17 val data not found. Run:\n" + " trackers download mot17 --split val --asset annotations,detections" + ) + + +def _run_tracker_on_sequence( + tracker, det_file: Path, output_file: Path, max_interpolation_gap: int = 0 +) -> None: + from trackers.core.sort.utils import interpolate_mot_gaps + from trackers.io.mot import _load_mot_file, _mot_frame_to_detections + + # reset() clears track list and resets the ID counter for all tracker types + tracker.reset() + + detections_data = _load_mot_file(det_file) + if not detections_data: + return + + max_frame = max(detections_data.keys()) + lines = [] + for frame_idx in range(1, max_frame + 1): + dets = sv.Detections.empty() + if frame_idx in detections_data: + dets = _mot_frame_to_detections(detections_data[frame_idx]) + tracked = tracker.update(dets) + if tracked.tracker_id is not None: + for i, tid in enumerate(tracked.tracker_id): + if tid < 0: + continue + x1, y1, x2, y2 = tracked.xyxy[i] + w, h = x2 - x1, y2 - y1 + conf = 1.0 + if tracked.confidence is not None: + conf = float(tracked.confidence[i]) + lines.append( + f"{frame_idx},{tid + 1},{x1:.2f},{y1:.2f}," + f"{w:.2f},{h:.2f},{conf:.4f},-1,-1,-1" + ) + + if max_interpolation_gap > 0: + lines = interpolate_mot_gaps(lines, max_gap=max_interpolation_gap) + + output_file.parent.mkdir(parents=True, exist_ok=True) + output_file.write_text("\n".join(lines) + "\n" if lines else "") + + +def _ensure_gt_symlink(data_dir: Path, seq: str) -> None: + """Create a gt/ symlink inside seq's directory if it is missing. + + Bundled SDP and DPM sequence directories do not include a gt/ subdirectory; + ground truth lives only in the corresponding FRCNN sibling (which is always + downloaded alongside the other detectors). This mirrors what + ``generate_detections.py`` does for YOLOWORLD/RFDETR directories. + + Args: + data_dir: MOT17-val root directory. + seq: Sequence directory name, e.g. ``"MOT17-02-SDP"``. + """ + gt_dir = data_dir / seq / "gt" + if gt_dir.exists(): + return + # Strip the detector suffix to find the FRCNN sibling + base = seq.rsplit("-", 1)[0] # "MOT17-02-SDP" → "MOT17-02" + frcnn_gt = data_dir / f"{base}-FRCNN" / "gt" + if frcnn_gt.exists(): + gt_dir.symlink_to(f"../{base}-FRCNN/gt") + + +def _run_eval( + params: dict, + sequences: list[str], + data_dir: Path, + tracker_name: str, + det_source: str = "frcnn", + show_progress: bool = False, +) -> dict: + """Evaluate tracker on sequences and return metric dict.""" + for seq in sequences: + _ensure_gt_symlink(data_dir, seq) + import tempfile + + from trackers.eval import evaluate_mot_sequence, evaluate_mot_sequences + + with tempfile.TemporaryDirectory() as _tmp: + output_dir = Path(_tmp) + + max_interp = params.get("max_interpolation_gap", 0) + tracker = _build_tracker(params, tracker_name) + + def _run_seq(seq: str) -> None: + det_file = data_dir / seq / "det" / "det.txt" + if det_file.exists(): + _run_tracker_on_sequence( + tracker=tracker, + det_file=det_file, + output_file=output_dir / f"{seq}.txt", + max_interpolation_gap=max_interp, + ) + + if show_progress: + with Progress( + TextColumn(" {task.description}"), + BarColumn(), + MofNCompleteColumn(), + console=_err, + transient=True, + ) as prog: + task_id = prog.add_task("", total=len(sequences)) + for seq in sequences: + prog.update(task_id, description=seq) + _run_seq(seq) + prog.advance(task_id) + else: + for seq in sequences: + _run_seq(seq) + + metrics_list = ["HOTA", "CLEAR", "Identity"] + try: + if len(sequences) == 1: + agg = evaluate_mot_sequence( + gt_path=data_dir / sequences[0] / "gt" / "gt.txt", + tracker_path=output_dir / f"{sequences[0]}.txt", + metrics=metrics_list, + threshold=0.5, + ) + else: + seqmap = output_dir / "seqmap.txt" + seqmap.write_text("\n".join(sequences) + "\n") + agg = evaluate_mot_sequences( + gt_dir=data_dir, + tracker_dir=output_dir, + seqmap=seqmap, + metrics=metrics_list, + threshold=0.5, + ).aggregate + except ValueError as exc: + # Tracker produced zero tracks (e.g. threshold too aggressive) → + # empty output file → evaluator raises ValueError. Return worst-case + # metrics so Optuna records the trial instead of logging it as failed. + if "MOT file is empty" in str(exc): + return {"HOTA": 0.0, "IDF1": 0.0, "MOTA": -100.0, "IDSW": 999999} + raise + + if not agg.HOTA: + raise RuntimeError( + "Evaluation returned no HOTA results — check sequences and gt paths" + ) + if not agg.Identity: + raise RuntimeError("Evaluation returned no Identity results") + if not agg.CLEAR: + raise RuntimeError("Evaluation returned no CLEAR results") + return { + "HOTA": agg.HOTA.HOTA * 100, + "IDF1": agg.Identity.IDF1 * 100, + "MOTA": agg.CLEAR.MOTA * 100, + "IDSW": int(agg.CLEAR.IDSW), + } + + +# --------------------------------------------------------------------------- +# Entry point +# --------------------------------------------------------------------------- + + +def _mp_worker( + storage_url: str, + n: int, + sequences: list[str], + data_dir_str: str, + tracker_name: str, + det_source: str = "frcnn", +) -> None: + """Multiprocessing worker: loads shared Optuna study and runs N trials. + + Runs in a separate process — module-level state (including the class-level + Kalman patch) is fully isolated, so concurrent workers never race. + + Args: + storage_url: SQLAlchemy URL for the shared SQLite study database. + n: Number of trials this worker should run. + sequences: MOT17-val sequence names to evaluate. + data_dir_str: Stringified path to the MOT17-val root (pickling-safe). + tracker_name: Which tracker to evaluate (sort | bytetrack | ocsort). + det_source: Detection source — "frcnn", "sdp", "dpm", "yolo", or "yoloworld". + """ + _data_dir = Path(data_dir_str) + _study = optuna.load_study(study_name=_STUDY_NAME, storage=storage_url) + + def _obj(trial: optuna.Trial) -> float: + params = _define_search_space(trial, tracker_name) + _apply_kalman_patch(params, tracker_name) + return _run_eval( + params=params, + sequences=sequences, + data_dir=_data_dir, + tracker_name=tracker_name, + det_source=det_source, + )["HOTA"] + + _study.optimize(_obj, n_trials=n, show_progress_bar=False) + + +def _validate_args(tracker: str, det_source: str) -> None: + """Raise ValueError if tracker is unrecognised. + + Any ``det_source`` value is accepted — known sources (frcnn, sdp, dpm, yoloworld, + rfdetr) map to their canonical tags via ``_DET_SOURCE_TO_TAG``; unknown values + are uppercased automatically, enabling custom detectors without any extra flags. + """ + if tracker not in _DEFAULTS: + raise ValueError( + f"Unknown tracker: {tracker!r}. Choose: {' | '.join(_DEFAULTS)}" + ) + + +def _resolve_sequences( + data_dir: str | None, + fast: bool, + det_source: str, +) -> tuple[Path, list[str]]: + """Locate MOT17 dir and enumerate sequences for the requested detector. + + Sequences are discovered by the directory suffix that matches the detector tag + (e.g. ``-FRCNN`` for frcnn, ``-YOLOWORLD`` for yoloworld). Each sequence dir must + have ``det/det.txt``; ground truth is found via ``gt/gt.txt`` (which may be a + symlink created by ``generate_detections.py``). + + Args: + data_dir: Explicit MOT17-val path, or None to auto-detect. + fast: If True, restrict to a single sequence for quick checks. + det_source: Detection source. Known values are looked up in + ``_DET_SOURCE_TO_TAG``; any other value is uppercased to form the tag, + so ``"mydet"`` resolves to ``MOT17-{N}-MYDET/`` without extra config. + + Returns: + Tuple of (data_dir Path, sequence name list). + """ + _data_dir = Path(data_dir) if data_dir else _find_data_dir() + tag = _DET_SOURCE_TO_TAG.get(det_source, det_source.upper()) + sequences = sorted( + d.name + for d in _data_dir.iterdir() + if d.is_dir() + and d.name.endswith(f"-{tag}") + and (d / "det" / "det.txt").exists() + ) + if not sequences: + hint = f" uv run python generate_detections.py --detector-tag {tag}" + raise FileNotFoundError( + f"No MOT17-*-{tag}/ sequences found in {_data_dir}.\n" + f"Generate detections with:\n{hint}" + ) + if fast: + fast_seq = [s for s in sequences if "MOT17-04" in s] + sequences = fast_seq[:1] if fast_seq else sequences[:1] + + return _data_dir, sequences + + +def _load_warm_start( + best_config_path: Path, + tracker: str, + det_source: str, + n_trials: int, +) -> dict | None: + """Load the previous best config as an Optuna warm-start point. + + Args: + best_config_path: Path to ``best_config.json``. + tracker: Tracker name (sort | bytetrack | ocsort). + det_source: Detection source (frcnn | sdp | dpm | yolo | yoloworld). + n_trials: Number of trials; warm-start is skipped when n_trials == 1. + + Returns: + Param dict to enqueue as the first trial, or None if unavailable. + """ + if n_trials == 1 or not best_config_path.exists(): + return None + try: + cfg = json.loads(best_config_path.read_text()) + entry = cfg.get(tracker, {}).get(det_source, {}) + warm = entry.get("config") + if warm is None: + _err.print( + f"[yellow]warn[/yellow] no prior best for {tracker}/{det_source}" + " in best_config.json — warm-start skipped, starting from defaults" + ) + return warm + except json.JSONDecodeError as exc: + _err.print( + f"[yellow]warn[/yellow] best_config.json is not valid JSON ({exc})" + " — warm-start skipped, starting from defaults" + ) + return None + + +def _make_progress_bar() -> Progress: + """Standard progress bar used for Optuna trial tracking.""" + return Progress( + TextColumn(" {task.description}"), + BarColumn(), + MofNCompleteColumn(), + TimeRemainingColumn(), + console=_err, + ) + + +def _run_parallel_study( + n_trials: int, + n_workers: int, + cpu_count: int, + initial_params: dict, + sequences: list[str], + data_dir: Path, + tracker: str, + det_source: str, +) -> optuna.Study: + """Run an Optuna study across ``n_workers`` processes via a SQLite back-end. + + Args: + n_trials: Total trials to distribute across workers. + n_workers: Number of parallel worker processes. + cpu_count: Logical CPU count (used only for the startup log line). + initial_params: Parameter dict enqueued as the first trial (warm start). + sequences: MOT17-val sequence names each worker evaluates. + data_dir: Root directory containing the sequences. + tracker: Tracker name (sort | bytetrack | ocsort). + det_source: Detection source tag (frcnn | sdp | dpm | yoloworld | rfdetr). + + Returns: + Completed ``optuna.Study`` loaded from the shared SQLite storage. + """ + import multiprocessing + import os + import tempfile + + fd, db_path = tempfile.mkstemp(suffix=".db") + os.close(fd) + storage_url = f"sqlite:///{db_path}" + try: + study = optuna.create_study( + study_name=_STUDY_NAME, + storage=storage_url, + direction="maximize", + sampler=optuna.samplers.TPESampler(seed=42), + ) + study.enqueue_trial(initial_params) + base, rem = divmod(n_trials, n_workers) + counts = [base + (1 if i < rem else 0) for i in range(n_workers)] + worker_args = [ + (storage_url, c, sequences, str(data_dir), tracker, det_source) + for c in counts + ] + print(f"[→ {n_workers} workers · {n_trials} trials · {cpu_count} cores]") + with multiprocessing.Pool(n_workers) as pool: + result = pool.starmap_async(_mp_worker, worker_args) + with _make_progress_bar() as prog: + _prefix = f"{tracker} | {det_source}" + tid = prog.add_task(f"{_prefix} | HOTA=?", total=n_trials) + while not result.ready(): + _poll = optuna.load_study( + study_name=_STUDY_NAME, storage=storage_url + ) + done = sum( + 1 + for t in _poll.trials + if t.state == optuna.trial.TrialState.COMPLETE + ) + best = _poll.best_value if done > 0 else 0.0 + prog.update( + tid, + completed=done, + description=f"{_prefix} | HOTA={best:.3f}", + ) + result.wait(timeout=2) + prog.update(tid, completed=n_trials) + result.get() # re-raise any worker exceptions + return optuna.load_study(study_name=_STUDY_NAME, storage=storage_url) + finally: + Path(db_path).unlink(missing_ok=True) + + +def _run_optuna_study( + n_trials: int, + n_jobs: int, + sequences: list[str], + data_dir: Path, + tracker: str, + det_source: str, + defaults: dict, + warm: dict | None, +) -> tuple[optuna.Study, int]: + """Create and run an Optuna study, using multiprocessing when n_workers > 1. + + Args: + n_trials: Total number of trials to run. + n_jobs: Worker count; -1 uses all CPUs, 1 disables multiprocessing. + sequences: MOT17-val sequence names to evaluate per trial. + data_dir: Root directory containing the sequences. + tracker: Tracker name (sort | bytetrack | ocsort). + det_source: Detection source (frcnn | sdp | dpm | yolo | yoloworld). + defaults: Default parameter dict (from ``default_config.json``) to enqueue. + warm: Prior best-config dict to warm-start from, or None. + + Returns: + Tuple of (completed Optuna Study, number of workers used). + """ + import os + + _check_config_drift(tracker) + cpu_count = os.cpu_count() or 1 + n_workers = min(n_trials, cpu_count if n_jobs == -1 else max(1, n_jobs)) + + if n_workers > 1: + study = _run_parallel_study( + n_trials=n_trials, + n_workers=n_workers, + cpu_count=cpu_count, + initial_params=warm or defaults, + sequences=sequences, + data_dir=data_dir, + tracker=tracker, + det_source=det_source, + ) + else: + study = optuna.create_study( + direction="maximize", + sampler=optuna.samplers.TPESampler(seed=42), + ) + study.enqueue_trial(defaults if n_trials == 1 else (warm or defaults)) + + def objective(trial: optuna.Trial) -> float: + params = _define_search_space(trial, tracker) + _apply_kalman_patch(params, tracker) + return _run_eval( + params=params, + sequences=sequences, + data_dir=data_dir, + tracker_name=tracker, + det_source=det_source, + )["HOTA"] + + callbacks: list = [] + _trial_prog: Progress | None = None + if n_trials > 1: + _trial_prog = _make_progress_bar() + _prefix = f"{tracker} | {det_source}" + _tid = _trial_prog.add_task(f"{_prefix} | HOTA=?", total=n_trials) + _trial_prog.start() + + def _trial_cb(study: optuna.Study, trial: optuna.trial.FrozenTrial) -> None: + best = study.best_value or 0.0 + _trial_prog.update( # type: ignore[union-attr] + _tid, advance=1, description=f"{_prefix} | HOTA={best:.3f}" + ) + + callbacks = [_trial_cb] + + try: + study.optimize( + objective, + n_trials=n_trials, + show_progress_bar=False, + callbacks=callbacks, + ) + finally: + if _trial_prog is not None: + _trial_prog.stop() + + return study, n_workers + + +def _save_best_if_improved( + best_config_path: Path, + tracker: str, + det_source: str, + best_metrics: dict, + best_params: dict, + n_trials: int, +) -> None: + """Persist best_params to best_config.json if HOTA improved over the previous run. + + The file is keyed as ``{tracker: {det_source: {hota, config}}}``, matching the + structure of ``defaults.json`` and ``search_space.json``. + + Args: + best_config_path: Path to ``best_config.json``. + tracker: Tracker name (sort | bytetrack | ocsort). + det_source: Detection source (frcnn | sdp | dpm | yolo | yoloworld). + best_metrics: Metric dict from the current run. + best_params: Corresponding Optuna best params. + n_trials: Saving is skipped for single-trial baseline evals. + """ + if n_trials <= 1: + return + cfg: dict = {} + if best_config_path.exists(): + try: + cfg = json.loads(best_config_path.read_text()) + except (json.JSONDecodeError, KeyError, TypeError, ValueError) as exc: + _err.print( + f"[yellow]warn[/yellow] could not read best_config.json ({exc})" + " — will overwrite with current result if it improves on 0.0" + ) + prev_hota = float(cfg.get(tracker, {}).get(det_source, {}).get("hota", 0.0)) + if best_metrics["HOTA"] > prev_hota: + cfg.setdefault(tracker, {})[det_source] = { + "hota": best_metrics["HOTA"], + "config": best_params, + } + best_config_path.write_text(json.dumps(cfg, indent=4)) + + +def _print_metrics( + best_metrics: dict, + best_params: dict, + elapsed: float, + n_trials: int, + n_workers: int, + tracker: str, + det_source: str, + sequences: list[str], +) -> None: + """Print __METRICS__, __CONFIG__, __ELAPSED__, __TRACKER__ lines to stdout.""" + raw_hota = best_metrics["HOTA"] + hota = raw_hota if math.isfinite(raw_hota) else 0.0 + if not math.isfinite(raw_hota): + _err.print( + f"[yellow]warn[/yellow] HOTA={raw_hota!r} — degenerate eval;" + " clamping to 0.0 so the campaign loop can revert this change" + ) + idf1 = best_metrics["IDF1"] + mota = best_metrics["MOTA"] + idsw = best_metrics["IDSW"] + print(f"\n__METRICS__: HOTA={hota:.3f} IDF1={idf1:.3f} MOTA={mota:.3f} IDSW={idsw}") + print(f"__CONFIG__: {json.dumps(best_params)}") + print( + f"__ELAPSED__: {elapsed:.1f}s __TRIALS__: {n_trials} __WORKERS__: {n_workers}" + ) + print( + f"__TRACKER__: {tracker} __DET_SOURCE__: {det_source}" + f" __SEQUENCES__: {','.join(sequences)}" + ) + + +def main( + tracker: str, + det_source: str, + n_trials: int = 1, + fast: bool = False, + data_dir: str | None = None, + n_jobs: int = -1, +) -> None: + """Run Optuna-based tracker search on MOT17-val. + + Args: + tracker: Which tracker to evaluate. One of: sort | bytetrack | ocsort. + det_source: Detection source — maps to MOT17-{N}-{TAG}/det/det.txt. + Bundled (no setup): frcnn, sdp, dpm. + Generated (run generate_detections.py first): yoloworld, rfdetr. + Any other value is uppercased and used directly as the directory tag, + e.g. ``mydet`` → ``MOT17-{N}-MYDET/``. + n_trials: Number of Optuna trials. 1 evaluates default params (campaign metric). + fast: Single sequence only (~3x faster, for development checks). + data_dir: MOT17 val directory. Auto-detected from standard cache paths if unset. + n_jobs: Worker processes. -1 uses all CPU cores (default). 1 = single process. + """ + _validate_args(tracker, det_source) + + t0 = time.time() + _data_dir, sequences = _resolve_sequences(data_dir, fast, det_source) + + best_config_path = Path(__file__).parent / "best_config.json" + warm = _load_warm_start(best_config_path, tracker, det_source, n_trials) + + study, n_workers = _run_optuna_study( + n_trials=n_trials, + n_jobs=n_jobs, + sequences=sequences, + data_dir=_data_dir, + tracker=tracker, + det_source=det_source, + defaults=_DEFAULTS[tracker], + warm=warm, + ) + + best_params = study.best_params + _apply_kalman_patch(best_params, tracker) + best_metrics = _run_eval( + params=best_params, + sequences=sequences, + data_dir=_data_dir, + tracker_name=tracker, + det_source=det_source, + show_progress=True, + ) + elapsed = time.time() - t0 + + _save_best_if_improved( + best_config_path=best_config_path, + tracker=tracker, + det_source=det_source, + best_metrics=best_metrics, + best_params=best_params, + n_trials=n_trials, + ) + _print_metrics( + best_metrics=best_metrics, + best_params=best_params, + elapsed=elapsed, + n_trials=n_trials, + n_workers=n_workers, + tracker=tracker, + det_source=det_source, + sequences=sequences, + ) + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/autotune/program.md b/autotune/program.md new file mode 100644 index 00000000..f5751cb3 --- /dev/null +++ b/autotune/program.md @@ -0,0 +1,234 @@ +# Campaign: Tracker algorithmic improvement on MOT17 + +## Goal + +**Research question**: Which architectural and algorithmic changes to the `{algo}` tracker improve multi-object tracking quality on MOT17 `{det_source}` detections? + +The campaign measures HOTA at default parameters after each code change. Optuna is a **validation tool**, not the goal — every candidate improvement is evaluated at default params first; Optuna confirms the signal is real and not a parameter artefact. + +The agent may rewrite the Kalman state representation, redesign the association pipeline, add new constructor parameters, and modify the shared `trackers/core/sort/utils.py` utilities (used by both SORT and ByteTrack). Every change must improve `{algo}` HOTA at default parameters to be kept. + +## Metric + +``` +command: cd autotune && uv run python optimize_tracking.py {algo} {det_source} --n-trials 1 2>&1 | grep "^__METRICS__" | grep -oE "HOTA=[0-9.]+" | cut -d= -f2 +direction: higher +``` + +## Guard + +``` +command: uv run pytest test/ -m "not integration" --ignore=test/scripts -q && cd autotune && uv run python guard.py +``` + +## Config + +`algo` and `det_source` are **defaults** substituted into `{algo}` / `{det_source}` in metric and guard commands. Override per run via clarification syntax: `/optimize run program.md "algo=sort"` or `/optimize run program.md "algo=ocsort det_source=dpm"`. + +``` +algo: bytetrack +det_source: sdp +max_iterations: 20 +agent_strategy: ml +scope_files: + - trackers/** + - autotune/optimize_tracking.py + - autotune/generate_detections.py + - autotune/search_space.json + - autotune/default_config.json +out_of_scope_files: + - trackers/eval/** + - trackers/datasets/** +compute: local +``` + +## Notes + +### Pre-flight checks + +All three setup steps must pass before starting the campaign loop: + +| Check | Command | Expected result | +| ------------- | ------------------------------------------------------------------------------ | ------------------------------------------------ | +| Dependencies | `uv sync --group optimize` | Resolves without error | +| MOT17 data | `trackers download mot17 --split val --asset annotations,detections,frames` | Downloads to `~/.cache/trackers/mot17/val/` | +| Metric sanity | `cd autotune && uv run python optimize_tracking.py {algo} {det_source} --fast` | Prints `__METRICS__: HOTA` within expected range | + +The guard uses `best_config.json` as the regression baseline — no separate seeding step required. The guard runs all three trackers (`bytetrack`, `sort`, `ocsort`) via `optimize_tracking.py sdp --n-trials 500` and fails if any tracker's HOTA drops more than 0.5% from its stored best. + +> **Custom detector** — create `MOT17-{N}-MYDET/det/det.txt` sibling dirs (see README.md Custom detections section), just pass the name as `det_source`: `uv run python optimize_tracking.py {algo} mydet` — unknown names are uppercased to form the directory tag (`MYDET`). + +### Evaluation protocol + +- **Primary metric**: HOTA on MOT17-val, `{det_source}` detections. Stops at `max_iterations`. +- **Secondary metrics** (logged, not gated): IDF1, MOTA, IDSW. A change that improves HOTA but worsens IDSW significantly is a warning sign — log it. +- **Fast mode** (`--fast`): single sequence (~3 s), sanity check only; campaign metric (`--n-trials 1`) always runs the full eval (~7 s, all sequences). + +**Current tuned baselines (SDP, 500 Optuna trials):** + +| Tracker | Tuned HOTA | Default HOTA | +| --------- | ---------- | ------------ | +| bytetrack | 59.131 | ~53.2 | +| sort | 56.129 | ~47.8 | +| ocsort | 57.867 | ~52.3 | + +### Hard boundaries — these invalidate the experiment if violated + +1. **Do not bypass `trackers.eval`**. The evaluation calls in `optimize_tracking.py` must go through `trackers.eval` unchanged — do not substitute custom metric code. +2. **Do not modify `trackers/eval/`**. The metric computation must be identical across all iterations. +3. **No ground-truth at inference time**. The tracker sees only detector output (`det/det.txt`). It must not read from `gt/` at any point. +4. **No external features**. The detector provides bounding boxes and confidence scores only. No appearance embeddings, no depth, no optical flow at association time unless derived purely from the bounding box sequence itself. +5. **The Kalman filter must remain a proper linear Kalman filter**. Learned components (neural prediction, learned motion model) require a separate research question and are out of scope here. +6. **API changes are allowed when well-justified**. Constructor signatures and `update()` may change if the change is architecturally motivated, accompanied by a rationale comment, and `optimize_tracking.py` is updated to match. The tracker must still implement `BaseTracker` and accept `sv.Detections` input. Do not rename the public tracker classes. + +### Optuna's role + +Optuna is used in two places only: + +1. **Pre-campaign baseline** (run once by the human before starting the loop): run `python optimize_tracking.py {algo} {det_source} --n-trials 200`, save the best param config to `best_config.json`. This gives a tuned ceiling for the *current* code. + +2. **Post-change validation** (optional, agent-initiated): after a code change is *kept* by the campaign loop, the agent may run a 50-trial mini-Optuna with the new code to confirm the improvement holds under tuned params and to update `best_config.json`. If tuned params *erase* the code change's improvement, that is a negative result — log it and revert. + +The campaign metric always measures at **default parameters** (~7 s per run). + +### Configuration files the agent may edit + +| File | What to change there | +| ---------------------- | ------------------------------------------------------------------------------------------------------------ | +| `search_space.json` | Add/remove Optuna parameters, widen/narrow ranges, switch to log scale | +| `default_config.json` | Update baseline defaults when a new parameter is added to the code | +| `optimize_tracking.py` | Update `_build_tracker` when constructor signatures change — that is the **only** reason to edit this script | + +### What the agent is free to change + +Within the scope files, the agent has full freedom to: + +- **Rewrite the Kalman state representation** — e.g., switch from xyxy-corners to (cx, cy, scale, ratio) or (cx, cy, w, h). Update `optimize_tracking.py` accordingly. +- **Redesign the association pipeline** — additional similarity metrics, gating strategies, cascade matching, score-weighted matching. +- **Integrate camera motion compensation** using `trackers/motion/estimator.py`; the infrastructure already exists in the codebase. +- **Add new constructor parameters** when motivated by the algorithm change; update `_build_tracker` and `_define_search_space` in `optimize_tracking.py` to expose the new knobs to Optuna. +- **Update `trackers/__init__.py`** if the public interface changes as a result of architectural improvements. +- Implement any classical (non-learned) tracking technique that improves HOTA. + +Each iteration must propose and implement **one atomic hypothesis**. Compound changes (two ideas in one commit) make it impossible to know what worked. + +### Failure logging + +Every reverted change is a result, not a failure. The `experiments.jsonl` log captures what was tried and what didn't improve HOTA. After the campaign, this log is the primary research artifact. + +If the campaign reaches `max_iterations` without achieving the goal, this is a valid research outcome. The `experiments.jsonl` log and final code state are the deliverables. + +### ByteTrack Phase 1 findings — already in the code (do not re-implement for ByteTrack) + +These hypotheses were implemented and kept in ByteTrack Phase 1. They are **not yet present in SORT or OC-SORT** and are promising first hypotheses for those trackers: + +| Hypothesis | HOTA delta on ByteTrack | +| ---------------------------------------------- | ----------------------- | +| Velocity decay β=0.95 during lost frames | +0.667% | +| Q inflation on missed frames (alpha=0.1) | +0.717% | +| Post-processing gap interpolation (max_gap=20) | +1.666% | +| P reset on re-detection after occlusion | +1.674% | + +### Research starting points — SOTA-inspired, not yet tried on all trackers + +Provided as inspiration, not a prescribed order. Hypotheses apply to the active `{algo}` tracker unless noted. + +**H-A: xcycsr state representation** Switch from xyxy-corners state `[x1,y1,x2,y2,vx1,vy1,vx2,vy2]` to center-based `[cx,cy,s,r,vcx,vcy,vs]` where `s = w*h` (area) and `r = w/h` (aspect ratio, often frozen). Corner velocities can be noisy when detectors shift boxes independently; center+area+ratio is more stable. Requires rewriting `kalman.py`, state↔bbox converters, and updating `optimize_tracking.py`. Applies to SORT and ByteTrack (both currently use xyxy corners). + +**H-B: Camera motion compensation (CMC)** Apply a frame-to-frame homography (ECC or sparse optical flow) to transform Kalman state predictions before association. The infrastructure exists at `trackers/motion/estimator.py` (`MotionEstimator`). BoT-SORT's primary gain on moving-camera sequences. Applies to SORT and ByteTrack. If full optical flow is too expensive for the 7 s eval budget, consider estimating motion from the detection cloud centroid shift. + +**H-C: Mahalanobis gate** Add a Mahalanobis distance gate using the predicted `P` matrix to discard geometrically impossible matches before the IoU similarity matrix is computed. Gate threshold is a tunable parameter. Applies to all trackers — modifies `get_iou_matrix` in `trackers/core/sort/utils.py`. + +**H-D: OC-SORT observation-centric velocity re-estimation** On re-detection after occlusion, compute a "virtual trajectory" between the last observation and the current detection (position difference / frames elapsed) and use this to update the velocity state, replacing the decayed estimate. Already implemented in ByteTrack; not in SORT. Pure position arithmetic — no appearance features needed. + +**H-E: Track confidence score** Maintain a per-track `score` (float) that initialises to detection confidence at birth, updates as a weighted average, decays during lost frames, resets on re-match. Use score as a minimum-gate for culling lost tracks early. Already in ByteTrack (partially); not in SORT. + +**H-F: GIoU or DIoU as association metric** Replace IoU with Generalised IoU (GIoU) or Distance IoU (DIoU) in the similarity matrix. Both recover near-miss associations that pure IoU scores as zero. Implementation: modify `get_iou_matrix` in `trackers/core/sort/utils.py` — affects all trackers that use it. + +**H-G: Adaptive confirmation threshold by object size** Make `minimum_consecutive_frames` adaptive: small/distant objects require more frames before confirmation; large/nearby objects are confirmed faster. New parameter: `size_conf_scale`. Modifies `get_alive_trackers` in `trackers/core/sort/utils.py` — affects all trackers. + +**H-H: Separate high/low IoU thresholds (parametrised correctly)** Expose `stage1_iou` and `stage2_iou` as separate Optuna parameters with the constraint `stage2_iou ≤ stage1_iou`, default `stage1_iou = 0.1` so baseline is not broken. Relevant to ByteTrack's two-stage association; SORT uses single-stage so this maps differently. + +### SORT Phase 1 findings — already in the code (do not re-implement) + +Campaign run on `bemch/auto-research` using 3-team parallel strategy (Kalman / Association / Lifecycle). Baseline: HOTA 53.217 → default-param result: 55.7 → tuned (500 trials): **57.7** (+8.4%). + +#### Kept changes + +| Hypothesis | Commit | HOTA delta at defaults | +| -------------------------------------------------------------------------------------- | --------- | ------------------------------------ | +| Kalman covariance dynamics (velocity_decay, q_miss_alpha, p_reset_threshold) | `8d66fba` | +0.98% | +| OC-SORT observation-centric velocity re-estimation (oru_threshold) | `de5704a` | +1.22% | +| DIoU replaces IoU in association matrix | `c4de2c6` | +0.17% | +| Confidence-weighted Hungarian assignment (conf_cost_weight) | `eefe13e` | enables Optuna headroom | +| IoU age discount for lost tracks (iou_age_weight) | `c094bcb` | enables Optuna headroom | +| Two-stage confidence-based association (high_conf_det_threshold, stage2_iou_threshold) | `e576b9e` | enables Optuna headroom | +| conf_cost_weight wiring + gap interpolation activation | `25d00c5` | activates existing feature | +| minimum_consecutive_frames 3→2 | `3555147` | faster confirmation | +| Align defaults with Optuna-tuned best_config | `ce69432` | +1.18% (single biggest default jump) | + +**New SORT constructor params**: `velocity_decay`, `q_miss_alpha`, `p_reset_threshold`, `oru_threshold`, `conf_cost_weight`, `iou_age_weight`, `high_conf_det_threshold`, `stage2_iou_threshold` + +#### Tuned best config (sort/sdp, 500 trials) + +```json +{ + "lost_track_buffer": 82, + "track_activation_threshold": 0.232, + "minimum_consecutive_frames": 2, + "minimum_iou_threshold": 0.0618, + "max_interpolation_gap": 31, + "velocity_decay": 0.524, + "q_miss_alpha": 0.79, + "p_reset_threshold": 12, + "oru_threshold": 2, + "conf_cost_weight": 0.36, + "iou_age_weight": 0.156, + "high_conf_det_threshold": 0.628, + "stage2_iou_threshold": 0.248 +} +``` + +### OC-SORT Phase 1 findings — already in the code (do not re-implement) + +Campaign run on `bemch/auto-research` using 3-team parallel strategy (Kalman / Association / Lifecycle + Codex co-pilot). Baseline: HOTA 53.351 → tuned (500 trials): **58.9** (+10.4%). + +#### Kept changes + +| Hypothesis | Commit | HOTA delta at defaults | +| -------------------------------------------------------------------------------- | ---------- | ----------------------- | +| Gap interpolation (max_interpolation_gap=20) | `(iter 1)` | +2.01% | +| Kalman Q/R/P scalar multipliers exposed to Optuna (q_scale, r_scale, p_scale) | `(iter 2)` | enables Optuna headroom | +| DIoU replaces IoU in OCM + OCR association stages | `(iter 3)` | within guard tolerance | +| Confidence-weighted Hungarian assignment (conf_cost_weight) | `(iter 4)` | +0.22% | +| IoU age discount for lost tracks in stage 1 (iou_age_weight) | `(iter 5)` | enables Optuna headroom | +| P reset to identity on re-detection after gap (p_reset_threshold) | `9960dd5` | enables Optuna headroom | +| Velocity decay + Q inflation during missed frames (velocity_decay, q_miss_alpha) | `9525885` | +0.43% to HOTA 58.905 | + +**New OC-SORT constructor params**: `conf_cost_weight`, `iou_age_weight`, `p_reset_threshold`, `velocity_decay`, `q_miss_alpha` + +#### Tuned best config (ocsort/sdp, 500 trials, HOTA=58.905) + +```json +{ + "lost_track_buffer": 74, + "minimum_consecutive_frames": 1, + "minimum_iou_threshold": 0.1488, + "direction_consistency_weight": 0.000618, + "high_conf_det_threshold": 0.6876, + "delta_t": 1, + "max_interpolation_gap": 42, + "q_scale": 0.7203, + "r_scale": 1.1889, + "p_scale": 0.0952, + "conf_cost_weight": 0.9699, + "iou_age_weight": 0.4279, + "p_reset_threshold": 8, + "velocity_decay": 0.926, + "q_miss_alpha": 0.5123 +} +``` + +### Agent warning — Kalman patch and state representation + +`_apply_kalman_patch` in `optimize_tracking.py` overwrites Q, R, and P with uniform identity-scaled matrices. If the state representation is changed (H-A), the patch must be redesigned to work with the new state dimension and matrix structure. diff --git a/autotune/search_space.json b/autotune/search_space.json new file mode 100644 index 00000000..90aaa130 --- /dev/null +++ b/autotune/search_space.json @@ -0,0 +1,259 @@ +{ + "_comment": "Optuna search space for each tracker. Edit this file to add/remove parameters or adjust ranges.", + "_edit_here": "Do not hard-code search ranges in optimize_tracking.py. Widen, narrow, or add new params here.", + "_types": { + "int": "trial.suggest_int(name, low, high [, log=true])", + "float": "trial.suggest_float(name, low, high [, log=true])", + "categorical": "trial.suggest_categorical(name, choices)" + }, + "bytetrack": { + "lost_track_buffer": { + "type": "int", + "low": 10, + "high": 120, + "log": true + }, + "track_activation_threshold": { + "type": "float", + "low": 0.1, + "high": 0.99 + }, + "minimum_consecutive_frames": { + "type": "int", + "low": 1, + "high": 5 + }, + "minimum_iou_threshold": { + "type": "float", + "low": 0.05, + "high": 0.5, + "log": true + }, + "stage2_iou_threshold": { + "type": "float", + "low": 0.01, + "high": 0.3, + "log": true + }, + "iou_age_weight": { + "type": "float", + "low": 0.0, + "high": 0.1 + }, + "high_conf_det_threshold": { + "type": "float", + "low": 0.15, + "high": 0.85 + }, + "q_scale": { + "type": "float", + "low": 0.0001, + "high": 0.1, + "log": true + }, + "r_scale": { + "type": "float", + "low": 0.01, + "high": 5.0, + "log": true + }, + "p_scale": { + "type": "float", + "low": 0.1, + "high": 50.0, + "log": true + }, + "velocity_decay": { + "type": "float", + "low": 0.5, + "high": 1.0 + }, + "q_miss_alpha": { + "type": "float", + "low": 0.0, + "high": 1.0 + }, + "max_interpolation_gap": { + "type": "int", + "low": 0, + "high": 60 + }, + "p_reset_threshold": { + "type": "int", + "low": 0, + "high": 30 + }, + "oru_threshold": { + "type": "int", + "low": 0, + "high": 15 + }, + "conf_cost_weight": { + "type": "float", + "low": 0.0, + "high": 1.0 + }, + "stage2_min_updates": { + "type": "int", + "low": 0, + "high": 15 + }, + "giou_blend": { + "type": "float", + "low": 0.0, + "high": 1.0 + } + }, + "sort": { + "lost_track_buffer": { + "type": "int", + "low": 5, + "high": 100, + "log": true + }, + "track_activation_threshold": { + "type": "float", + "low": 0.05, + "high": 0.99 + }, + "minimum_consecutive_frames": { + "type": "int", + "low": 1, + "high": 5 + }, + "minimum_iou_threshold": { + "type": "float", + "low": 0.05, + "high": 0.7, + "log": true + }, + "max_interpolation_gap": { + "type": "int", + "low": 0, + "high": 60 + }, + "velocity_decay": { + "type": "float", + "low": 0.5, + "high": 1.0 + }, + "q_miss_alpha": { + "type": "float", + "low": 0.0, + "high": 2.0 + }, + "p_reset_threshold": { + "type": "int", + "low": 0, + "high": 30 + }, + "oru_threshold": { + "type": "int", + "low": 0, + "high": 15 + }, + "conf_cost_weight": { + "type": "float", + "low": 0.0, + "high": 1.0 + }, + "iou_age_weight": { + "type": "float", + "low": 0.0, + "high": 0.2 + }, + "high_conf_det_threshold": { + "type": "float", + "low": 0.0, + "high": 0.8 + }, + "stage2_iou_threshold": { + "type": "float", + "low": 0.01, + "high": 0.3, + "log": true + } + }, + "ocsort": { + "lost_track_buffer": { + "type": "int", + "low": 10, + "high": 80, + "log": true + }, + "minimum_consecutive_frames": { + "type": "int", + "low": 1, + "high": 5 + }, + "minimum_iou_threshold": { + "type": "float", + "low": 0.05, + "high": 0.5, + "log": true + }, + "direction_consistency_weight": { + "type": "float", + "low": 0.0, + "high": 1.0 + }, + "high_conf_det_threshold": { + "type": "float", + "low": 0.1, + "high": 0.7 + }, + "delta_t": { + "type": "int", + "low": 1, + "high": 5 + }, + "max_interpolation_gap": { + "type": "int", + "low": 0, + "high": 60 + }, + "q_scale": { + "type": "float", + "low": 0.001, + "high": 10.0, + "log": true + }, + "r_scale": { + "type": "float", + "low": 0.01, + "high": 100.0, + "log": true + }, + "p_scale": { + "type": "float", + "low": 0.01, + "high": 100.0, + "log": true + }, + "conf_cost_weight": { + "type": "float", + "low": 0.0, + "high": 1.0 + }, + "iou_age_weight": { + "type": "float", + "low": 0.0, + "high": 0.5 + }, + "p_reset_threshold": { + "type": "int", + "low": 0, + "high": 30 + }, + "velocity_decay": { + "type": "float", + "low": 0.5, + "high": 1.0 + }, + "q_miss_alpha": { + "type": "float", + "low": 0.0, + "high": 2.0 + } + } +} diff --git a/autotune/visualize_detections.py b/autotune/visualize_detections.py new file mode 100644 index 00000000..b4c7783e --- /dev/null +++ b/autotune/visualize_detections.py @@ -0,0 +1,249 @@ +#!/usr/bin/env python3 +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""visualize_detections.py — Overlay detections on MOT17 sequence frames. + +Reads ``det/det.txt`` and ``img1/`` from one or more sequence directories and +writes annotated JPEG frames to ``vis/`` inside each sequence directory. + +Usage: + cd autotune + uv run python visualize_detections.py MOT17-04-YOLOWORLD + uv run python visualize_detections.py MOT17-04-YOLOWORLD MOT17-02-RFDETR + uv run python visualize_detections.py --det-source yoloworld + uv run python visualize_detections.py MOT17-04-YOLOWORLD --conf 0.3 + +Output: + {seq_dir}/vis/{frame_stem}.jpg (one file per frame, skipped if exists) + +Notes: + - Box colour is a red→green gradient keyed to confidence (0 = red, 1 = green). + - Confidence score is shown as a small label above each box. + - Bundled detectors (DPM, SDP) fall back to the FRCNN sibling for frames. +""" + +from __future__ import annotations + +import re +from pathlib import Path + +import cv2 +import fire +import numpy as np +import supervision as sv +from rich.console import Console +from rich.progress import track + +console = Console() + +_DEFAULT_DATA_DIR = Path("./mot17/val") + + +def _conf_color(conf: float) -> tuple[int, int, int]: + """BGR colour interpolated from red (0) to green (1). + + Args: + conf: Confidence score in [0, 1]. + + Returns: + BGR tuple suitable for cv2 drawing functions. + """ + r = int(255 * (1.0 - conf)) + g = int(255 * conf) + return (0, g, r) + + +def _load_detections(det_txt: Path, conf_threshold: float) -> dict[int, sv.Detections]: + """Parse a MOT-format det.txt; return a dict keyed by 1-based frame index. + + Args: + det_txt: Path to ``det/det.txt``. + conf_threshold: Minimum confidence — lower detections are dropped. + + Returns: + Mapping from frame index to ``sv.Detections`` for that frame. + """ + by_frame: dict[int, list[list[float]]] = {} + for line in det_txt.read_text().splitlines(): + line = line.strip() + if not line: + continue + parts = line.split(",") + if len(parts) < 7: + continue + frame_idx = int(parts[0]) + conf = float(parts[6]) + if conf < conf_threshold: + continue + x, y, w, h = map(float, parts[2:6]) + by_frame.setdefault(frame_idx, []).append([x, y, x + w, y + h, conf]) + + result: dict[int, sv.Detections] = {} + for frame_idx, rows in by_frame.items(): + arr = np.array(rows, dtype=np.float32) + result[frame_idx] = sv.Detections(xyxy=arr[:, :4], confidence=arr[:, 4]) + return result + + +def _annotate_frame( + image: np.ndarray, + detections: sv.Detections | None, +) -> np.ndarray: + """Draw detection boxes and confidence labels on a BGR image. + + Each box is coloured on a red→green gradient keyed to its confidence score. + + Args: + image: BGR image array (H, W, 3). + detections: Detections for this frame, or ``None`` if no boxes. + + Returns: + Annotated BGR image. + """ + if detections is None or len(detections) == 0: + return image + + if detections.confidence is None: + return image + for (x1, y1, x2, y2), conf in zip(detections.xyxy, detections.confidence): + color = _conf_color(float(conf)) + cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), color, 2) + label = f"{conf:.2f}" + cv2.putText( + image, + label, + (int(x1), max(int(y1) - 4, 10)), + cv2.FONT_HERSHEY_SIMPLEX, + 0.4, + color, + 1, + cv2.LINE_AA, + ) + return image + + +def _process_sequence( + seq_dir: Path, + conf_threshold: float, +) -> int: + """Visualise one sequence; returns the number of frames written. + + Args: + seq_dir: Path to the sequence directory (must contain ``det/det.txt``). + ``img1/`` is used if present; otherwise the FRCNN sibling's frames + are used automatically (covers bundled DPM/SDP sequences). + conf_threshold: Minimum confidence to include a detection. + + Returns: + Number of frames actually written. + """ + det_txt = seq_dir / "det" / "det.txt" + vis_dir = seq_dir / "vis" + + img_dir = seq_dir / "img1" + if not img_dir.exists(): + base = seq_dir.name.rsplit("-", 1)[0] # "MOT17-02-DPM" → "MOT17-02" + frcnn_img1 = seq_dir.parent / f"{base}-FRCNN" / "img1" + if frcnn_img1.exists(): + img_dir = frcnn_img1 + else: + console.print(f"[yellow] skip {seq_dir.name} — no img1/[/yellow]") + return 0 + if not det_txt.exists(): + console.print(f"[yellow] skip {seq_dir.name} — no det/det.txt[/yellow]") + return 0 + + vis_dir.mkdir(exist_ok=True) + det_by_frame = _load_detections(det_txt, conf_threshold) + + img_files = sorted(img_dir.glob("*.jpg")) + sorted(img_dir.glob("*.png")) + if not img_files: + console.print(f"[yellow] skip {seq_dir.name} — no images in img1/[/yellow]") + return 0 + + def _frame_idx(p: Path) -> int: + m = re.search(r"(\d+)", p.stem) + return int(m.group(1)) if m else 0 + + written = 0 + for img_path in track(img_files, description=f" {seq_dir.name}", console=console): + out_path = vis_dir / f"{img_path.stem}.jpg" + image = cv2.imread(str(img_path)) + if image is None: + continue + + image = _annotate_frame(image, det_by_frame.get(_frame_idx(img_path))) + cv2.imwrite(str(out_path), image, [cv2.IMWRITE_JPEG_QUALITY, 90]) + written += 1 + + return written + + +def main( + *sequences: str, + det_source: str = "", + data_dir: str = str(_DEFAULT_DATA_DIR), + conf: float = 0.1, +) -> None: + """Visualise detections for one or more MOT17 sequence directories. + + Args: + *sequences: Sequence directory names or absolute paths (e.g. + ``MOT17-04-RFDETR``). Relative names resolve under ``data_dir``. + If omitted, ``--det-source`` selects all matching sequences. + det_source: Detector tag shorthand (``yoloworld``, ``rfdetr``, ``frcnn``, + ``sdp``, ``dpm``). Selects all matching sequences when no positional + sequences are given. + data_dir: Root directory containing MOT17-val sequences (default: cwd). + conf: Minimum detection confidence to include (default 0.1). + + Examples: + Single sequence:: + + uv run python visualize_detections.py MOT17-04-YOLOWORLD + + All RF-DETR sequences:: + + uv run python visualize_detections.py --det-source rfdetr + """ + root = Path(data_dir).expanduser() + seq_dirs: list[Path] = [] + + if sequences: + for s in sequences: + p = Path(s).expanduser() + seq_dirs.append(p if p.is_absolute() else root / s) + elif det_source: + tag = det_source.upper() + seq_dirs = sorted( + d for d in root.iterdir() if d.is_dir() and d.name.endswith(f"-{tag}") + ) + if not seq_dirs: + root_abs = root.resolve() + dirs = [d.name for d in root_abs.iterdir() if d.is_dir()] + console.print(f"[red]No sequences found for tag -{tag} in {root_abs}[/red]") + console.print(f"Directories found: {dirs or '(none)'}") + return + else: + console.print("[red]Provide a sequence name or --det-source TAG[/red]") + raise SystemExit(1) + + total = 0 + for seq_dir in seq_dirs: + if not seq_dir.exists(): + console.print(f"[red]Not found: {seq_dir}[/red]") + continue + console.print(f"[cyan]{seq_dir.name}[/cyan] → {seq_dir / 'vis'}") + written = _process_sequence(seq_dir, conf) + total += written + console.print(f" [green]✓ {written} frames[/green]") + + console.print(f"\n[bold green]Done — {total} total frames[/bold green]") + + +if __name__ == "__main__": + fire.Fire(main) diff --git a/docs/trackers/comparison.md b/docs/trackers/comparison.md index f877f68b..81a8911f 100644 --- a/docs/trackers/comparison.md +++ b/docs/trackers/comparison.md @@ -13,8 +13,7 @@ Pedestrian tracking with crowded scenes and frequent occlusions. Strongly tests !!! info - Parameters were tuned on the validation set. Results are reported on the - test set via Codabench submission. Detections come from a YOLOX model. + Parameters were tuned on the validation set. Results are reported on the test set via Codabench submission. Detections come from a YOLOX model. === "Default" @@ -72,8 +71,7 @@ Sports broadcast tracking with fast motion, camera pans, and similar-looking tar !!! info - Parameters were tuned on the validation set. Results are reported on the - test set via Codabench submission. Detections come from a YOLOX model. + Parameters were tuned on the validation set. Results are reported on the test set via Codabench submission. Detections come from a YOLOX model. === "Default" @@ -131,9 +129,7 @@ Long sequences with dense interactions and partial occlusions. Tests long-term I !!! info - Parameters were tuned on the train set. Results are reported on the test - set. SoccerNet-tracking has no validation split. This dataset provides - oracle (ground-truth) detections. + Parameters were tuned on the train set. Results are reported on the test set. SoccerNet-tracking has no validation split. This dataset provides oracle (ground-truth) detections. === "Default" @@ -191,15 +187,11 @@ Group dancing tracking with uniform appearance, diverse motions, and extreme art !!! warning - DanceTrack test set evaluation is currently unavailable because CodaLab, which hosted - the benchmark, has been [discontinued](https://docs.codabench.org/dev/Newsletters_Archive/CodaLab-in-2025/). - Migration to Codabench is [in progress](https://github.com/DanceTrack/DanceTrack/issues/42). - Results below use the validation set instead. + DanceTrack test set evaluation is currently unavailable because CodaLab, which hosted the benchmark, has been [discontinued](https://docs.codabench.org/dev/Newsletters_Archive/CodaLab-in-2025/). Migration to Codabench is [in progress](https://github.com/DanceTrack/DanceTrack/issues/42). Results below use the validation set instead. !!! info - Parameters were tuned on the train set. Results are reported on the - validation set. This dataset provides oracle (ground-truth) detections. + Parameters were tuned on the train set. Results are reported on the validation set. This dataset provides oracle (ground-truth) detections. === "Default" @@ -250,15 +242,11 @@ Group dancing tracking with uniform appearance, diverse motions, and extreme art ### Detections -Each dataset uses one of two detection sources: oracle detections (ground-truth -bounding boxes provided by the dataset) or model detections (produced by a YOLOX -detector following the ByteTrack procedure). The source is noted per dataset above. +Each dataset uses one of two detection sources: oracle detections (ground-truth bounding boxes provided by the dataset) or model detections (produced by a YOLOX detector following the ByteTrack procedure). The source is noted per dataset above. ### Tuning -Best parameters per tracker and dataset were found via grid search, selecting the -configuration with the highest HOTA. Tuning and evaluation always use separate data -splits to reflect real-world usage: +Best parameters per tracker and dataset were found via grid search, selecting the configuration with the highest HOTA. Tuning and evaluation always use separate data splits to reflect real-world usage: - Train + validation + test: tune on validation, report on test. - Train + validation: tune on train, report on validation. diff --git a/docs/trackers/ocsort.md b/docs/trackers/ocsort.md index 180da3c1..f4e16c37 100644 --- a/docs/trackers/ocsort.md +++ b/docs/trackers/ocsort.md @@ -6,10 +6,7 @@ comments: true ## Overview -OC-SORT remains Simple, Online, and Real-Time like ([SORT](../sort/tracker.md)) but improves robustness during occlusion and non-linear motion. -It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three mechanisms to enhance tracking. These -mechanisms help having better Kalman Filter parameters after an occlusion, add a term to the association process to incorporate how consistent is the direction with the new association with respect to the tracks' previous direction and add a second-stage association step between the last observation of unmatched tracks and the unmatched observations after the usual association to attempt to recover tracks that were lost -due to object stopping or short-term occlusion. +OC-SORT remains Simple, Online, and Real-Time like ([SORT](../sort/tracker.md)) but improves robustness during occlusion and non-linear motion. It recognizes limitations from SORT and the linear motion assumption of the Kalman filter, and adds three mechanisms to enhance tracking. These mechanisms help having better Kalman Filter parameters after an occlusion, add a term to the association process to incorporate how consistent is the direction with the new association with respect to the tracks' previous direction and add a second-stage association step between the last observation of unmatched tracks and the unmatched observations after the usual association to attempt to recover tracks that were lost due to object stopping or short-term occlusion. ## Comparison diff --git a/pyproject.toml b/pyproject.toml index c662c6ee..8ed86893 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -75,6 +75,18 @@ mypy-types = [ "types-requests>=2.32.0.20250328", ] +optimize = [ + "optuna[rdb]>=3.1.0", + "fire>=0.6.0", + "inference>=0.9.0", + "inference-models>=0.19.0", + "ultralytics", # dependency in inference + "rfdetr>=1.6", +] + +[tool.uv.sources] +onnx-simplifier = { git = "https://github.com/daquexian/onnx-simplifier.git", tag = "v0.4.10" } + [build-system] requires = ["hatchling"] build-backend = "hatchling.build" diff --git a/test/core/test_bytetrack_smoke.py b/test/core/test_bytetrack_smoke.py new file mode 100644 index 00000000..7e891056 --- /dev/null +++ b/test/core/test_bytetrack_smoke.py @@ -0,0 +1,164 @@ +# ------------------------------------------------------------------------ +# Trackers +# Copyright (c) 2026 Roboflow. All Rights Reserved. +# Licensed under the Apache License, Version 2.0 [see LICENSE for details] +# ------------------------------------------------------------------------ + +"""Lightweight smoke tests for ByteTrackTracker and the shared get_iou_matrix utility. + +These tests run without any external data downloads and without the integration +test marker, so they execute on every guard invocation during the campaign loop. + +Goals: +- Catch bugs in ByteTrackTracker.update() before the expensive ~7s metric eval runs. +- Guard get_iou_matrix against cross-tracker regressions: both ByteTrack and SORT + depend on this shared utility, so a ByteTrack-motivated change that breaks it + would silently regress SORT with no other test catching it. +""" + +from __future__ import annotations + +import numpy as np +import pytest +import supervision as sv + +from trackers import ByteTrackTracker +from trackers.core.bytetrack.kalman import ByteTrackKalmanBoxTracker +from trackers.core.sort.kalman import SORTKalmanBoxTracker +from trackers.core.sort.utils import get_iou_matrix + + +def _make_dets(boxes: np.ndarray, confidence: float = 0.9) -> sv.Detections: + return sv.Detections( + xyxy=boxes.astype(np.float32), + confidence=np.full(len(boxes), confidence, dtype=np.float32), + ) + + +class TestByteTrackTrackerUpdate: + """End-to-end update() smoke tests — exercises the full predict/associate cycle.""" + + def test_stable_detections_produce_confirmed_track_ids(self) -> None: + """Objects detected consistently across frames should receive confirmed IDs.""" + tracker = ByteTrackTracker(minimum_consecutive_frames=2) + boxes = np.array([[10.0, 20.0, 50.0, 60.0], [100.0, 100.0, 150.0, 150.0]]) + + result = None + for _ in range(5): + result = tracker.update(_make_dets(boxes)) + + assert result is not None + assert result.tracker_id is not None, ( + "tracker_id must be set after stable detections" + ) + assert any(tid >= 0 for tid in result.tracker_id), ( + "at least one track should be confirmed (tid >= 0)" + ) + + def test_update_returns_sv_detections(self) -> None: + """update() return type must be sv.Detections regardless of input.""" + tracker = ByteTrackTracker() + boxes = np.array([[0.0, 0.0, 100.0, 100.0]]) + result = tracker.update(_make_dets(boxes)) + assert isinstance(result, sv.Detections) + + def test_empty_detections_do_not_crash(self) -> None: + """update() with empty Detections must not raise, must return sv.Detections.""" + tracker = ByteTrackTracker() + for _ in range(3): + result = tracker.update(sv.Detections.empty()) + assert isinstance(result, sv.Detections) + + def test_reset_clears_tracks(self) -> None: + """reset() must discard all active tracks; next update starts fresh.""" + tracker = ByteTrackTracker(minimum_consecutive_frames=1) + boxes = np.array([[10.0, 20.0, 50.0, 60.0]]) + + for _ in range(3): + tracker.update(_make_dets(boxes)) + + tracker.reset() + result = tracker.update(_make_dets(boxes)) + # After reset + one frame, no track should be confirmed yet + # (minimum_consecutive_frames=1 means confirmed immediately after 1 hit) + assert result.tracker_id is not None + + @pytest.mark.parametrize("n_boxes", [1, 5, 20]) + def test_various_detection_counts(self, n_boxes: int) -> None: + """update() must handle 1, 5, or 20 detections without raising.""" + tracker = ByteTrackTracker() + rng = np.random.default_rng(seed=0) + x1 = rng.uniform(0, 400, size=(n_boxes,)) + y1 = rng.uniform(0, 300, size=(n_boxes,)) + boxes = np.stack([x1, y1, x1 + 40, y1 + 40], axis=1) + result = tracker.update(_make_dets(boxes)) + assert isinstance(result, sv.Detections) + + +class TestGetIouMatrix: + """Unit tests for the shared get_iou_matrix utility. + + Both ByteTrackTracker and SORTTracker depend on this function. Any + modification to it for ByteTrack purposes is caught here for SORT too. + """ + + def test_bytetrack_kalman_same_box_iou_is_one(self) -> None: + """Predicted box == detection box should yield IoU ≈ 1.0.""" + box = np.array([10.0, 20.0, 50.0, 60.0]) + tracker_obj = ByteTrackKalmanBoxTracker(bbox=box) + detection_boxes = np.array([[10.0, 20.0, 50.0, 60.0]]) + + iou_mat = get_iou_matrix([tracker_obj], detection_boxes) + + assert iou_mat.shape == (1, 1) + assert iou_mat[0, 0] == pytest.approx(1.0, abs=1e-5) + + def test_sort_kalman_same_box_iou_is_one(self) -> None: + """SORT Kalman: predicted box == detection box should yield IoU ≈ 1.0.""" + box = np.array([10.0, 20.0, 50.0, 60.0]) + tracker_obj = SORTKalmanBoxTracker(bbox=box) + detection_boxes = np.array([[10.0, 20.0, 50.0, 60.0]]) + + iou_mat = get_iou_matrix([tracker_obj], detection_boxes) + + assert iou_mat.shape == (1, 1) + assert iou_mat[0, 0] == pytest.approx(1.0, abs=1e-5) + + def test_non_overlapping_boxes_yield_non_positive_similarity(self) -> None: + """Non-overlapping boxes must yield similarity ≤ 0 (DIoU penalises centre distance).""" # noqa: E501 + box = np.array([0.0, 0.0, 10.0, 10.0]) + tracker_obj = ByteTrackKalmanBoxTracker(bbox=box) + detection_boxes = np.array([[100.0, 100.0, 200.0, 200.0]]) + + iou_mat = get_iou_matrix([tracker_obj], detection_boxes) + + assert iou_mat[0, 0] <= 0.0 + + def test_empty_trackers_returns_zero_matrix(self) -> None: + """Empty tracker list must return a (0, N) zero matrix.""" + detection_boxes = np.array([[10.0, 20.0, 50.0, 60.0]]) + iou_mat = get_iou_matrix([], detection_boxes) + assert iou_mat.shape == (0, 1) + + def test_empty_detections_returns_zero_matrix(self) -> None: + """Empty detection array must return a (N, 0) zero matrix.""" + box = np.array([10.0, 20.0, 50.0, 60.0]) + tracker_obj = ByteTrackKalmanBoxTracker(bbox=box) + iou_mat = get_iou_matrix([tracker_obj], np.zeros((0, 4), dtype=np.float32)) + assert iou_mat.shape == (1, 0) + + def test_output_shape_multiple_trackers_and_detections(self) -> None: + """Shape must be (n_trackers, n_detections) for arbitrary sizes.""" + boxes = np.array( + [ + [0.0, 0.0, 40.0, 40.0], + [50.0, 50.0, 90.0, 90.0], + [200.0, 200.0, 300.0, 300.0], + ] + ) + trackers = [ByteTrackKalmanBoxTracker(bbox=b) for b in boxes] + detection_boxes = np.array([[0.0, 0.0, 40.0, 40.0], [50.0, 50.0, 90.0, 90.0]]) + + iou_mat = get_iou_matrix(trackers, detection_boxes) + + assert iou_mat.shape == (3, 2) diff --git a/trackers/core/bytetrack/kalman.py b/trackers/core/bytetrack/kalman.py index c091e067..62c86206 100644 --- a/trackers/core/bytetrack/kalman.py +++ b/trackers/core/bytetrack/kalman.py @@ -32,6 +32,37 @@ class ByteTrackKalmanBoxTracker: """ count_id = 0 + # Velocity decay factor applied per frame when the track is unmatched. + # Attenuates velocity to prevent unbounded linear drift during occlusion, + # keeping the predicted box near the last observed position. Standard + # technique from OC-SORT / BoT-SORT that directly improves association + # accuracy (AssA) for re-identification after occlusion gaps. + velocity_decay: float = 0.95 + # Process noise inflation rate for lost tracks. During occlusion the true + # object motion is unknown, so uncertainty should grow faster than in the + # steady-state. Each missed frame multiplies the process noise covariance + # by (1 + q_miss_alpha * time_since_update), widening the predicted + # covariance and increasing Kalman gain on re-appearance — the filter + # trusts the fresh measurement more. Orthogonal to velocity_decay which + # acts on the state mean, not the covariance. + q_miss_alpha: float = 0.1 + # Minimum number of missed frames before resetting P on re-detection. + # When a lost track is re-matched after a long gap, the accumulated + # error covariance P carries stale position-velocity cross-correlations + # from the Q-inflated lost period. Resetting P to a fresh initial state + # after the Kalman update gives the filter a clean slate for velocity + # estimation while keeping the measurement-corrected position from the + # update step. Set to 0 to disable the reset. + p_reset_threshold: int = 5 + # Minimum gap (in missed frames) before applying Observation-Centric + # Re-Update (ORU) on re-detection. When a lost track is re-matched + # after >= oru_threshold frames, the filter state is rolled back to the + # last observed state and virtual observations are replayed along the + # linearly interpolated trajectory from last_observation to the new + # detection. This re-estimates velocity correctly instead of relying on + # stale/decayed velocity from the lost period. Set to 0 to disable ORU. + # Technique from OC-SORT (Cao et al., 2023). + oru_threshold: int = 2 state: NDArray[np.float32] F: NDArray[np.float32] H: NDArray[np.float32] @@ -73,6 +104,11 @@ def __init__(self, bbox: np.ndarray): self.state[2] = bbox[2] self.state[3] = bbox[3] + # ORU: last observed bbox and frozen Kalman state for re-update + self.last_observation: np.ndarray = bbox.copy() + self._frozen_state: dict[str, NDArray[np.float32]] | None = None + self._was_observed: bool = True + # Basic constant velocity model self._initialize_kalman_filter() @@ -99,14 +135,93 @@ def _initialize_kalman_filter(self) -> None: # Error covariance matrix (P) self.P = np.eye(8, dtype=np.float32) + def _freeze(self) -> None: + """Save Kalman filter state at the moment of last observation. + + Called when a track transitions from observed to lost. The frozen + state is used by ORU to roll back and replay virtual observations + on re-detection. + """ + self._frozen_state = { + "state": self.state.copy(), + "P": self.P.copy(), + } + + def _apply_oru(self, new_bbox: np.ndarray) -> None: + """Observation-Centric Re-Update: restore frozen state and replay. + + Rolls back to the frozen Kalman state, then replays + ``time_since_update`` predict+update cycles with linearly + interpolated virtual observations from ``last_observation`` to + ``new_bbox``. The final real update is done by the caller. + + Args: + new_bbox: The new detection in ``[x1, y1, x2, y2]`` format. + """ + if self._frozen_state is None: + return + + # Restore frozen state + self.state = self._frozen_state["state"] + self.P = self._frozen_state["P"] + self._frozen_state = None + + time_gap = self.time_since_update + last = self.last_observation + delta = (new_bbox - last) / time_gap + + # Replay virtual predict+update cycles (all but the last step; + # the final real update is performed by the normal update() call). + for i in range(1, time_gap): + # Predict one step + self.state = (self.F @ self.state).astype(np.float32) + self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + + # Virtual observation at interpolated position + virtual_obs = (last + i * delta).reshape((4, 1)).astype(np.float32) + s_mat = self.H @ self.P @ self.H.T + self.R + k_mat = (self.P @ self.H.T @ np.linalg.inv(s_mat)).astype(np.float32) + y_res = virtual_obs - self.H @ self.state + self.state = (self.state + k_mat @ y_res).astype(np.float32) + ident = np.eye(8, dtype=np.float32) + self.P = ((ident - k_mat @ self.H) @ self.P).astype(np.float32) + + # After ORU, do one final predict so the caller's update() call + # sees a properly predicted state at the current time step. + self.state = (self.F @ self.state).astype(np.float32) + self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + def predict(self) -> None: """ Predict the next state of the bounding box (applies the state transition). """ # Predict state self.state = (self.F @ self.state).astype(np.float32) + + # When the track is lost, inflate process noise to reflect growing + # uncertainty about the object's true position. The base Q is NOT + # mutated — a scaled copy is used for this prediction step only. + if self.time_since_update > 0: + q_scale = 1.0 + self.q_miss_alpha * self.time_since_update + q_eff = self.Q * q_scale + else: + q_eff = self.Q + # Predict error covariance - self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + self.P = (self.F @ self.P @ self.F.T + q_eff).astype(np.float32) + + # Attenuate velocity components when track is lost (unmatched). + # Indices 4-7 are (vx1, vy1, vx2, vy2). Applied *after* the + # transition so the current frame's prediction still uses full + # velocity; only subsequent predictions on still-lost tracks see + # progressively slower motion. + if self.time_since_update > 0: + self.state[4:8] *= self.velocity_decay + + # Freeze state on transition from observed → lost (first miss) + if self.time_since_update == 0 and self._was_observed: + self._freeze() + self._was_observed = False # Increase time since update self.time_since_update += 1 @@ -118,8 +233,21 @@ def update(self, bbox: np.ndarray) -> None: Args: bbox: Detected bounding box in the form [x1, y1, x2, y2]. """ + was_lost_for = self.time_since_update + + # ORU: if track was lost long enough, roll back and replay virtual + # observations so the Kalman filter re-learns velocity from the + # interpolated trajectory rather than using the stale/decayed one. + if ( + self.oru_threshold > 0 + and was_lost_for >= self.oru_threshold + and self._frozen_state is not None + ): + self._apply_oru(bbox) + self.time_since_update = 0 self.number_of_successful_updates += 1 + self._was_observed = True # Kalman Gain S = self.H @ self.P @ self.H.T + self.R @@ -136,6 +264,16 @@ def update(self, bbox: np.ndarray) -> None: identity_matrix = np.eye(8, dtype=np.float32) self.P = ((identity_matrix - K @ self.H) @ self.P).astype(np.float32) + # Reset P after long occlusion to clear stale cross-covariances. + # The Kalman update above already used the inflated P (high gain, + # trusting the fresh measurement). Resetting afterwards gives the + # filter a clean velocity-estimation slate for subsequent frames. + if self.p_reset_threshold > 0 and was_lost_for >= self.p_reset_threshold: + self.P = np.eye(8, dtype=np.float32) + + # Update last observation for future ORU interpolation + self.last_observation = bbox.copy() + def get_state_bbox(self) -> np.ndarray: """ Returns the current bounding box estimate from the state vector. diff --git a/trackers/core/bytetrack/tracker.py b/trackers/core/bytetrack/tracker.py index 3792266c..954edb26 100644 --- a/trackers/core/bytetrack/tracker.py +++ b/trackers/core/bytetrack/tracker.py @@ -16,6 +16,53 @@ ) +def _giou_matrix( + track_bboxes: np.ndarray, + det_bboxes: np.ndarray, +) -> np.ndarray: + """Compute Generalized IoU between all track-detection pairs. + + GIoU extends standard IoU by adding a penalty term for the gap between + the two boxes and their smallest enclosing box, providing a non-zero + gradient even for non-overlapping boxes. + + Args: + track_bboxes: Track predicted bboxes, shape ``(N, 4)`` in + ``[x1, y1, x2, y2]`` format. + det_bboxes: Detection bboxes, shape ``(M, 4)`` in + ``[x1, y1, x2, y2]`` format. + + Returns: + GIoU matrix of shape ``(N, M)`` with values in ``[-1, 1]``. + """ + # Intersection corners + x1_i = np.maximum(track_bboxes[:, None, 0], det_bboxes[None, :, 0]) + y1_i = np.maximum(track_bboxes[:, None, 1], det_bboxes[None, :, 1]) + x2_i = np.minimum(track_bboxes[:, None, 2], det_bboxes[None, :, 2]) + y2_i = np.minimum(track_bboxes[:, None, 3], det_bboxes[None, :, 3]) + inter = np.maximum(0.0, x2_i - x1_i) * np.maximum(0.0, y2_i - y1_i) + + # Individual box areas and union + area_t = (track_bboxes[:, 2] - track_bboxes[:, 0]) * ( + track_bboxes[:, 3] - track_bboxes[:, 1] + ) + area_d = (det_bboxes[:, 2] - det_bboxes[:, 0]) * ( + det_bboxes[:, 3] - det_bboxes[:, 1] + ) + union = area_t[:, None] + area_d[None, :] - inter + iou = np.where(union > 0, inter / union, 0.0) + + # Smallest enclosing box area + x1_c = np.minimum(track_bboxes[:, None, 0], det_bboxes[None, :, 0]) + y1_c = np.minimum(track_bboxes[:, None, 1], det_bboxes[None, :, 1]) + x2_c = np.maximum(track_bboxes[:, None, 2], det_bboxes[None, :, 2]) + y2_c = np.maximum(track_bboxes[:, None, 3], det_bboxes[None, :, 3]) + enc_area = (x2_c - x1_c) * (y2_c - y1_c) + + giou = iou - np.where(enc_area > 0, (enc_area - union) / enc_area, 0.0) + return giou.astype(np.float32) + + class ByteTrackTracker(BaseTracker): """ByteTrack operates online by processing all detector outputs, categorizing them by confidence thresholds to enable a two-stage association process. High-score boxes @@ -51,6 +98,21 @@ class ByteTrackTracker(BaseTracker): threshold, tracks are assigned `tracker_id` of `-1`. minimum_iou_threshold: `float` specifying IoU threshold for associating detections to existing tracks. Higher values require more overlap. + stage2_iou_threshold: `float` specifying IoU threshold for stage-2 + low-confidence association. Lower values are more permissive when + reviving tracks from low-confidence detections. + iou_age_weight: `float` specifying how much to discount IoU + similarity for lost tracks in stage-1 assignment ranking. + Each lost track's IoU row is scaled by + ``1 / (1 + iou_age_weight * lost_frames)`` where + ``lost_frames = max(0, time_since_update - 1)``. The discount + biases the solver to prefer active tracks over stale + predictions (reducing identity switches) but only affects + ranking — the minimum-IoU threshold is checked against the + *raw* IoU so that valid matches are never rejected by the + discount alone. ``0`` disables the discount. Only applied in + stage 1; stage 2 is unaffected so that lost tracks can still + recover via low-confidence detections. high_conf_det_threshold: `float` specifying threshold for separating high and low confidence detections in the two-stage association. """ @@ -64,7 +126,12 @@ def __init__( track_activation_threshold: float = 0.7, minimum_consecutive_frames: int = 2, minimum_iou_threshold: float = 0.1, + stage2_iou_threshold: float = 0.05, + iou_age_weight: float = 0.03, high_conf_det_threshold: float = 0.6, + conf_cost_weight: float = 0.0, + stage2_min_updates: int = 0, + giou_blend: float = 0.0, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -72,8 +139,13 @@ def __init__( self.maximum_frames_without_update = int(frame_rate / 30.0 * lost_track_buffer) self.minimum_consecutive_frames = minimum_consecutive_frames self.minimum_iou_threshold = minimum_iou_threshold + self.stage2_iou_threshold = stage2_iou_threshold + self.iou_age_weight = iou_age_weight self.track_activation_threshold = track_activation_threshold self.high_conf_det_threshold = high_conf_det_threshold + self.conf_cost_weight = conf_cost_weight + self.stage2_min_updates = stage2_min_updates + self.giou_blend = giou_blend self.tracks: list[ByteTrackKalmanBoxTracker] = [] def update( @@ -120,9 +192,55 @@ def update( low_boxes = detection_boxes[low_indices] # Step 1: associate high-confidence detections to all tracks - iou_matrix = get_iou_matrix(self.tracks, high_boxes) + raw_iou = get_iou_matrix(self.tracks, high_boxes) + + # GIoU blend: mix standard IoU with normalized GIoU (∈ [-1,1] mapped + # to [0,1]) to provide proximity credit for near-miss cases where the + # Kalman prediction drifted slightly below the IoU threshold. The + # threshold gate always uses *raw* IoU so the blend only affects + # ranking, never filtering. + if self.giou_blend > 0 and raw_iou.size > 0: + track_bboxes = np.array( + [t.get_state_bbox() for t in self.tracks], dtype=np.float32 + ) + giou = _giou_matrix(track_bboxes, high_boxes) + iou_matrix = ( + (1.0 - self.giou_blend) * raw_iou + self.giou_blend * (giou + 1.0) / 2.0 + ).astype(np.float32) + else: + iou_matrix = raw_iou + + # Age discount: scale down IoU for lost tracks so the assignment + # algorithm prefers active tracks over stale predictions. This + # reduces identity switches from a drifted prediction "stealing" + # a detection that should go to the correct active track. + # + # The discount is applied only to the cost matrix used by the + # solver for ranking; the threshold check uses the *raw* IoU so + # that valid matches are never rejected by the discount alone. + # Only tracks that have been lost for at least 1 frame are + # discounted — freshly-seen tracks (time_since_update == 1 after + # predict, i.e. lost_frames == 0) are never penalised. + if self.iou_age_weight > 0 and iou_matrix.size > 0: + lost_frames = np.array( + [max(0, t.time_since_update - 1) for t in self.tracks], + dtype=np.float32, + ) + discount = 1.0 / (1.0 + self.iou_age_weight * lost_frames) + solver_iou = iou_matrix * discount[:, np.newaxis] + else: + solver_iou = iou_matrix + + # Confidence boost: scale up solver IoU for higher-confidence detections + # so the Hungarian assignment prefers confident detections over uncertain + # ones when IoU values are close. The boost only affects ranking; the + # threshold gate still uses raw IoU so valid matches are never blocked. + if self.conf_cost_weight > 0 and solver_iou.size > 0 and len(high_indices) > 0: + conf_boost = 1.0 + self.conf_cost_weight * confidences[high_indices] + solver_iou = solver_iou * conf_boost[np.newaxis, :] + matched, unmatched_tracks, unmatched_high = self._get_associated_indices( - iou_matrix, self.minimum_iou_threshold + solver_iou, self.minimum_iou_threshold, raw_similarity=raw_iou ) for row, col in matched: @@ -136,12 +254,25 @@ def update( out_det_indices.append(int(high_indices[col])) out_tracker_ids.append(track.tracker_id) - remaining_tracks = [self.tracks[i] for i in unmatched_tracks] + # Stage-2 maturity gate: only established tracks participate in low-conf + # recovery. Young tracks (number_of_successful_updates < stage2_min_updates) + # are excluded to prevent tentative ghost tracks from being incorrectly + # recovered via low-confidence detections. When stage2_min_updates == 0 + # (default) all unmatched tracks participate, preserving the original behaviour. + if self.stage2_min_updates > 0: + remaining_tracks = [ + self.tracks[i] + for i in unmatched_tracks + if self.tracks[i].number_of_successful_updates + >= self.stage2_min_updates + ] + else: + remaining_tracks = [self.tracks[i] for i in unmatched_tracks] # Step 2: associate low-confidence detections to remaining tracks iou_matrix = get_iou_matrix(remaining_tracks, low_boxes) matched, _, unmatched_low = self._get_associated_indices( - iou_matrix, self.minimum_iou_threshold + iou_matrix, self.stage2_iou_threshold ) for row, col in matched: @@ -191,32 +322,43 @@ def _get_associated_indices( self, similarity_matrix: np.ndarray, min_similarity_thresh: float, + raw_similarity: np.ndarray | None = None, ) -> tuple[list[tuple[int, int]], set[int], set[int]]: - """ - Associate detections to tracks based on Similarity (IoU) using the - Jonker-Volgenant algorithm approach with no initialization instead of the - Hungarian algorithm as mentioned in the SORT paper, but it solves the - assignment problem in an optimal way. + """Associate detections to tracks based on similarity (IoU). + + Uses the Jonker-Volgenant algorithm (via ``linear_sum_assignment``) + to solve the assignment optimally. Args: - similarity_matrix: Similarity matrix between tracks (rows) and detections (columns). - min_similarity_thresh: Minimum similarity threshold for a valid match. + similarity_matrix: Similarity matrix between tracks (rows) and + detections (columns). Used by the solver for ranking. + min_similarity_thresh: Minimum similarity threshold for a valid + match. + raw_similarity: Optional unmodified similarity matrix. When + provided, the threshold check uses this matrix instead of + ``similarity_matrix`` so that solver-side discounts (e.g. + the age discount) cannot reject otherwise valid matches. Returns: - Matched indices (list of (tracker_idx, detection_idx)), indices of - unmatched tracks, indices of unmatched detections. - """ # noqa: E501 + Matched indices (list of (tracker_idx, detection_idx)), indices + of unmatched tracks, indices of unmatched detections. + """ matched_indices = [] n_tracks, n_detections = similarity_matrix.shape unmatched_tracks = set(range(n_tracks)) unmatched_detections = set(range(n_detections)) + # Use raw similarity for threshold gating when available + thresh_matrix = ( + raw_similarity if raw_similarity is not None else similarity_matrix + ) + if n_tracks > 0 and n_detections > 0: row_indices, col_indices = linear_sum_assignment( similarity_matrix, maximize=True ) for row, col in zip(row_indices, col_indices): - if similarity_matrix[row, col] >= min_similarity_thresh: + if thresh_matrix[row, col] >= min_similarity_thresh: matched_indices.append((row, col)) unmatched_tracks.remove(row) unmatched_detections.remove(col) diff --git a/trackers/core/ocsort/tracker.py b/trackers/core/ocsort/tracker.py index f84fa7b6..7155d71f 100644 --- a/trackers/core/ocsort/tracker.py +++ b/trackers/core/ocsort/tracker.py @@ -14,6 +14,7 @@ _build_direction_consistency_matrix_batch, _get_iou_matrix, ) +from trackers.core.sort.utils import _compute_diou_matrix from trackers.utils.state_representations import XCYCSRStateEstimator @@ -57,6 +58,15 @@ class OCSORTTracker(BaseTracker): delta_t: `int` specifying number of past frames to use for velocity estimation. Higher values provide more stable direction estimates during occlusion. + conf_cost_weight: `float` specifying how strongly detection confidence + breaks IoU ties in the Hungarian assignment. A value of ``0.0`` + disables the feature (pure IoU+direction). Positive values boost + higher-confidence detections in the solver matrix while keeping + the gate check on the raw IoU. + iou_age_weight: `float` specifying how strongly a track's age (frames + since last update) discounts its solver cost. A value of ``0.0`` + disables the feature. Stale lost tracks get a lower solver score, + pushing them to OCR recovery while fresh tracks win stage 1. """ tracker_id = "ocsort" @@ -70,6 +80,11 @@ def __init__( direction_consistency_weight: float = 0.2, high_conf_det_threshold: float = 0.6, delta_t: int = 3, + conf_cost_weight: float = 0.0, + iou_age_weight: float = 0.0, + p_reset_threshold: int = 0, + velocity_decay: float = 1.0, + q_miss_alpha: float = 0.0, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -80,6 +95,11 @@ def __init__( self.direction_consistency_weight = direction_consistency_weight self.high_conf_det_threshold = high_conf_det_threshold self.delta_t = delta_t + self.conf_cost_weight = conf_cost_weight + self.iou_age_weight = iou_age_weight + self.p_reset_threshold = p_reset_threshold + self.velocity_decay = velocity_decay + self.q_miss_alpha = q_miss_alpha self.tracks: list[OCSORTTracklet] = [] self.frame_count = 0 @@ -89,6 +109,8 @@ def _get_associated_indices( self, iou_matrix: np.ndarray, direction_consistency_matrix: np.ndarray, + confidences: np.ndarray | None = None, + track_ages: np.ndarray | None = None, ) -> tuple[list[tuple[int, int]], list[int], list[int]]: """ Associate detections to tracks based on IOU. @@ -96,6 +118,12 @@ def _get_associated_indices( Args: iou_matrix: IOU cost matrix. direction_consistency_matrix: Direction of the tracklet consistency cost matrix. + confidences: Optional detection confidence scores `(n_detections,)` used + to break IoU ties when `conf_cost_weight > 0`. Gate check still uses + raw `iou_matrix`. + track_ages: Optional `(n_tracks,)` array of `time_since_update` values. + When `iou_age_weight > 0`, stale tracks are discounted in the solver + cost so they fall through to OCR. Gate check is unaffected. Returns: matched_indices: List of (track_index, detection_index) tuples for @@ -115,6 +143,26 @@ def _get_associated_indices( iou_matrix + self.direction_consistency_weight * direction_consistency_matrix ) + if ( + self.iou_age_weight > 0 + and track_ages is not None + and track_ages.size > 0 + ): + age_discount = 1.0 / ( + 1.0 + self.iou_age_weight * np.maximum(0, track_ages - 1) + ) + cost_matrix = (cost_matrix * age_discount[:, np.newaxis]).astype( + np.float32 + ) + if ( + self.conf_cost_weight > 0 + and confidences is not None + and confidences.size > 0 + ): + conf_boost = 1.0 + self.conf_cost_weight * confidences + cost_matrix = (cost_matrix * conf_boost[np.newaxis, :]).astype( + np.float32 + ) row_indices, col_indices = linear_sum_assignment(cost_matrix, maximize=True) for row, col in zip(row_indices, col_indices): if iou_matrix[row, col] >= self.minimum_iou_threshold: @@ -140,6 +188,9 @@ def _spawn_new_tracklets(self, boxes: np.ndarray) -> None: xyxy, delta_t=self.delta_t, state_estimator_class=self.state_estimator_class, + p_reset_threshold=self.p_reset_threshold, + velocity_decay=self.velocity_decay, + q_miss_alpha=self.q_miss_alpha, ) ) @@ -181,6 +232,7 @@ def update(self, detections: sv.Detections) -> sv.Detections: tracker.predict() predicted_boxes = np.array([t.get_state_bbox() for t in self.tracks]) + track_ages = np.array([t.time_since_update for t in self.tracks]) iou_matrix = _get_iou_matrix(predicted_boxes, detection_boxes) direction_consistency_matrix = self._compute_direction_consistency_matrix( @@ -189,7 +241,9 @@ def update(self, detections: sv.Detections) -> sv.Detections: # 1st association (OCM) matched_indices, unmatched_tracks, unmatched_detections = ( - self._get_associated_indices(iou_matrix, direction_consistency_matrix) + self._get_associated_indices( + iou_matrix, direction_consistency_matrix, confidences, track_ages + ) ) for row, col in matched_indices: @@ -205,13 +259,15 @@ def update(self, detections: sv.Detections) -> sv.Detections: last_observation_of_tracks = np.array( [self.tracks[t].last_observation for t in unmatched_tracks] ) - ocr_iou_matrix = sv.box_iou_batch( + ocr_iou_matrix = _compute_diou_matrix( last_observation_of_tracks, detection_boxes[unmatched_detections], ) ocr_matched, ocr_unmatched_tracks, ocr_unmatched_dets = ( self._get_associated_indices( - ocr_iou_matrix, np.zeros_like(ocr_iou_matrix) + ocr_iou_matrix, + np.zeros_like(ocr_iou_matrix), + confidences[unmatched_detections], ) ) diff --git a/trackers/core/ocsort/tracklet.py b/trackers/core/ocsort/tracklet.py index 89aa2b64..1669fb9a 100644 --- a/trackers/core/ocsort/tracklet.py +++ b/trackers/core/ocsort/tracklet.py @@ -45,6 +45,9 @@ def __init__( initial_bbox: np.ndarray, state_estimator_class: type[BaseStateEstimator] = XCYCSRStateEstimator, delta_t: int = 3, + p_reset_threshold: int = 0, + velocity_decay: float = 1.0, + q_miss_alpha: float = 0.0, ) -> None: """Initialize tracklet with first detection. @@ -56,8 +59,17 @@ def __init__( delta_t: Number of timesteps back to look for velocity estimation. Higher values use observations further in the past to estimate motion direction, providing more stable velocity estimates. + p_reset_threshold: Minimum missed frames before resetting P to + identity on re-detection. ``0`` disables the reset. + velocity_decay: Multiplicative factor applied to velocity components + ``kf.x[4:7]`` each missed frame. ``1.0`` disables. + q_miss_alpha: Per-frame Q inflation rate for missed frames. + ``Q_eff = Q * (1 + alpha * time_since_update)``. ``0.0`` disables. """ self.age = 0 + self.p_reset_threshold = p_reset_threshold + self.velocity_decay = velocity_decay + self.q_miss_alpha = q_miss_alpha # Initialize state estimator (wraps KalmanFilter + state repr) self.kalman_filter: BaseStateEstimator = state_estimator_class(initial_bbox) @@ -232,6 +244,8 @@ def update(self, bbox: np.ndarray | None) -> None: if previous_box is not None: self.velocity = self._compute_velocity(previous_box, bbox) + gap = self.time_since_update + # Check if we need to unfreeze (was lost, now observed) if not self._observed and self._frozen_state is not None: self._unfreeze(bbox) @@ -241,6 +255,12 @@ def update(self, bbox: np.ndarray | None) -> None: # without ORU this is the normal measurement update) self.kalman_filter.update(bbox) + # P reset: after a long gap, discard stale accumulated uncertainty + # so velocity estimation starts fresh from the re-detection. + if self.p_reset_threshold > 0 and gap >= self.p_reset_threshold: + kf = self.kalman_filter.kf + kf.P = np.eye(kf.P.shape[0], dtype=np.float32) + self._observed = True self.time_since_update = 0 self.number_of_successful_consecutive_updates += 1 @@ -260,10 +280,30 @@ def predict(self) -> np.ndarray: Returns: Predicted bounding box `[x1, y1, x2, y2]`. """ + is_lost = self.time_since_update > 0 + + # Q inflation: widen predicted covariance for lost tracks so the filter + # trusts fresh measurements more on re-detection. + if self.q_miss_alpha > 0 and is_lost: + q_orig = self.kalman_filter.kf.Q.copy() + self.kalman_filter.kf.Q = ( + q_orig * (1.0 + self.q_miss_alpha * self.time_since_update) + ).astype(np.float32) + self.kalman_filter.predict() + + if self.q_miss_alpha > 0 and is_lost: + self.kalman_filter.kf.Q = q_orig + + # Velocity decay: attenuate velocity components to reduce prediction drift. + if self.velocity_decay < 1.0 and is_lost: + self.kalman_filter.kf.x[4:7] = ( + self.kalman_filter.kf.x[4:7] * self.velocity_decay + ).astype(np.float32) + self.age += 1 - if self.time_since_update > 0: + if is_lost: self.number_of_successful_consecutive_updates = 0 self.time_since_update += 1 diff --git a/trackers/core/ocsort/utils.py b/trackers/core/ocsort/utils.py index 6f612a7d..abefcb8c 100644 --- a/trackers/core/ocsort/utils.py +++ b/trackers/core/ocsort/utils.py @@ -10,7 +10,8 @@ from __future__ import annotations import numpy as np -import supervision as sv + +from trackers.core.sort.utils import _compute_diou_matrix def _speed_direction_batch( @@ -127,7 +128,7 @@ def _get_iou_matrix(track_boxes: np.ndarray, detection_boxes: np.ndarray) -> np. n_tracks = track_boxes.shape[0] n_detections = detection_boxes.shape[0] if n_tracks > 0 and n_detections > 0: - iou_matrix = sv.box_iou_batch(track_boxes, detection_boxes) + iou_matrix = _compute_diou_matrix(track_boxes, detection_boxes) else: iou_matrix = np.zeros((n_tracks, n_detections), dtype=np.float32) return iou_matrix diff --git a/trackers/core/sort/kalman.py b/trackers/core/sort/kalman.py index 144fde58..1ff64762 100644 --- a/trackers/core/sort/kalman.py +++ b/trackers/core/sort/kalman.py @@ -45,7 +45,14 @@ def get_next_tracker_id(cls) -> int: cls.count_id += 1 return next_id - def __init__(self, bbox: NDArray[np.float64]) -> None: + def __init__( + self, + bbox: NDArray[np.float64], + velocity_decay: float = 0.95, + q_miss_alpha: float = 0.0, + p_reset_threshold: int = 0, + oru_threshold: int = 0, + ) -> None: # Initialize with a temporary ID of -1 # Will be assigned a real ID when the track is considered mature self.tracker_id = -1 @@ -56,6 +63,27 @@ def __init__(self, bbox: NDArray[np.float64]) -> None: # Number of frames since the last update self.time_since_update = 0 + # Kalman dynamics hyper-parameters + # velocity_decay: shrinks velocity components each missed frame to prevent + # runaway linear extrapolation during occlusions (technique from OC-SORT). + self.velocity_decay = velocity_decay + # q_miss_alpha: multiplicative Q-inflation rate for missed frames — + # widens predicted covariance so the filter trusts fresh measurements + # more on re-detection. Orthogonal to velocity_decay. + self.q_miss_alpha = q_miss_alpha + # p_reset_threshold: if a track was lost for >= this many frames before + # re-detection, reset P to identity after the update step so stale + # uncertainty is discarded. 0 disables the reset. + self.p_reset_threshold = p_reset_threshold + # oru_threshold: observation-centric re-estimation update — on re-detection + # after >= this many missed frames, override the Kalman velocity with a + # virtual trajectory computed from (current - last_observed) / gap. + # Technique from OC-SORT. 0 disables. + self.oru_threshold = oru_threshold + + # Store last observed bbox for observation-centric velocity re-estimation + self._last_observed_bbox: NDArray[np.float32] | None = None + # For simplicity, we keep a small state vector: # (x, y, x2, y2, vx, vy, vx2, vy2). # We'll store the bounding box in "self.state" @@ -67,6 +95,7 @@ def __init__(self, bbox: NDArray[np.float64]) -> None: self.state[1, 0] = bbox_float[1] self.state[2, 0] = bbox_float[2] self.state[3, 0] = bbox_float[3] + self._last_observed_bbox = bbox_float[:4].copy() # Basic constant velocity model self._initialize_kalman_filter() @@ -98,10 +127,24 @@ def predict(self) -> None: """ Predict the next state of the bounding box (applies the state transition). """ + # Velocity decay: shrink velocity components when the track is lost to + # prevent runaway linear extrapolation during occlusions. + if self.time_since_update > 0 and self.velocity_decay < 1.0: + self.state[4:8] = (self.state[4:8] * self.velocity_decay).astype(np.float32) + # Predict state self.state = (self.F @ self.state).astype(np.float32) + + # Q inflation for missed frames: widen uncertainty so the filter gives + # higher weight to fresh measurements on re-detection. + if self.time_since_update > 0 and self.q_miss_alpha > 0.0: + q_scale = 1.0 + self.q_miss_alpha * self.time_since_update + q_eff = (self.Q * q_scale).astype(np.float32) + else: + q_eff = self.Q + # Predict error covariance - self.P = (self.F @ self.P @ self.F.T + self.Q).astype(np.float32) + self.P = (self.F @ self.P @ self.F.T + q_eff).astype(np.float32) # Increase time since update self.time_since_update += 1 @@ -113,9 +156,26 @@ def update(self, bbox: NDArray[np.float64]) -> None: Args: bbox: Detected bounding box in the form [x1, y1, x2, y2]. """ + was_lost_for = self.time_since_update self.time_since_update = 0 self.number_of_successful_updates += 1 + # Observation-centric velocity re-estimation (OC-SORT technique): + # on re-detection after a gap, compute a "virtual trajectory" velocity + # from (current_bbox - last_observed_bbox) / gap and override the + # (decayed) Kalman velocity estimate. + if ( + self.oru_threshold > 0 + and was_lost_for >= self.oru_threshold + and self._last_observed_bbox is not None + ): + bbox_f = bbox.astype(np.float32) + virtual_vel = (bbox_f[:4] - self._last_observed_bbox) / was_lost_for + self.state[4, 0] = virtual_vel[0] + self.state[5, 0] = virtual_vel[1] + self.state[6, 0] = virtual_vel[2] + self.state[7, 0] = virtual_vel[3] + # Kalman Gain S: NDArray[np.float32] = (self.H @ self.P @ self.H.T + self.R).astype( np.float32 @@ -137,6 +197,14 @@ def update(self, bbox: NDArray[np.float64]) -> None: identity_matrix: NDArray[np.float32] = np.eye(8, dtype=np.float32) self.P = ((identity_matrix - K @ self.H) @ self.P).astype(np.float32) + # Store this observation for future ORU velocity re-estimation + self._last_observed_bbox = bbox.astype(np.float32)[:4].copy() + + # P reset: after a long gap, discard stale accumulated uncertainty so + # velocity estimation starts fresh from the re-detection. + if self.p_reset_threshold > 0 and was_lost_for >= self.p_reset_threshold: + self.P = np.eye(8, dtype=np.float32) + def get_state_bbox(self) -> NDArray[np.float32]: """ Returns the current bounding box estimate from the state vector. diff --git a/trackers/core/sort/tracker.py b/trackers/core/sort/tracker.py index 5e0f2a5f..aa5443b6 100644 --- a/trackers/core/sort/tracker.py +++ b/trackers/core/sort/tracker.py @@ -51,6 +51,41 @@ class SORTTracker(BaseTracker): threshold, tracks are assigned `tracker_id` of `-1`. minimum_iou_threshold: `float` specifying IoU threshold for associating detections to existing tracks. Higher values require more overlap. + velocity_decay: `float` in [0, 1] applied to the Kalman velocity + components each missed frame, preventing runaway linear extrapolation + during occlusions. `1.0` disables the feature. + q_miss_alpha: `float` ≥ 0 scaling the per-frame Q inflation rate for + lost tracks (`Q_eff = Q * (1 + alpha * time_since_update)`). Wider + uncertainty on re-detection gives higher Kalman gain to fresh + measurements. `0.0` disables the feature. + p_reset_threshold: `int` minimum number of missed frames before resetting + the error covariance P to identity on re-detection. Discards stale + accumulated uncertainty after long gaps. `0` disables the reset. + oru_threshold: `int` minimum missed frames before applying observation- + centric velocity re-estimation on re-detection. Computes a virtual + trajectory velocity from (current - last_observed) / gap to replace + the decayed Kalman velocity. Technique from OC-SORT. `0` disables. + conf_cost_weight: `float` specifying how strongly detection confidence + breaks IoU ties in the Hungarian assignment. A value of ``0.0`` + disables the feature (pure IoU). Positive values boost + higher-confidence detections in the solver matrix while keeping + the IoU gate unchanged, so no invalid matches are accepted. + iou_age_weight: `float` specifying how much to discount DIoU + similarity for lost tracks. Each lost track's row is scaled by + ``1 / (1 + iou_age_weight * lost_frames)`` where + ``lost_frames = max(0, time_since_update - 1)``. This biases the + solver to prefer active tracks over stale predictions, reducing + identity switches from drifted predictions. The threshold gate + always uses raw DIoU. ``0`` disables the discount. + high_conf_det_threshold: `float` specifying confidence threshold for + two-stage association. Detections above this threshold are matched + in stage 1; remaining low-confidence detections are matched in + stage 2 against unmatched tracks using ``stage2_iou_threshold``. + ``0.0`` disables two-stage (all detections go to stage 1). + stage2_iou_threshold: `float` specifying DIoU threshold for the + second association stage with low-confidence detections. Typically + lower than ``minimum_iou_threshold`` to be more permissive when + recovering tracks from occluded or partially-visible objects. """ tracker_id = "sort" @@ -62,6 +97,14 @@ def __init__( track_activation_threshold: float = 0.25, minimum_consecutive_frames: int = 3, minimum_iou_threshold: float = 0.3, + velocity_decay: float = 0.95, + q_miss_alpha: float = 0.0, + p_reset_threshold: int = 0, + oru_threshold: int = 0, + conf_cost_weight: float = 0.0, + iou_age_weight: float = 0.0, + high_conf_det_threshold: float = 0.0, + stage2_iou_threshold: float = 0.05, ) -> None: # Calculate maximum frames without update based on lost_track_buffer and # frame_rate. This scales the buffer based on the frame rate to ensure @@ -70,40 +113,55 @@ def __init__( self.minimum_consecutive_frames = minimum_consecutive_frames self.minimum_iou_threshold = minimum_iou_threshold self.track_activation_threshold = track_activation_threshold + self.velocity_decay = velocity_decay + self.q_miss_alpha = q_miss_alpha + self.p_reset_threshold = p_reset_threshold + self.oru_threshold = oru_threshold + self.conf_cost_weight = conf_cost_weight + self.iou_age_weight = iou_age_weight + self.high_conf_det_threshold = high_conf_det_threshold + self.stage2_iou_threshold = stage2_iou_threshold # Active trackers self.trackers: list[SORTKalmanBoxTracker] = [] - def _get_associated_indices( - self, iou_matrix: np.ndarray, detection_boxes: np.ndarray + @staticmethod + def _match( + similarity: np.ndarray, + min_thresh: float, + raw_similarity: np.ndarray | None = None, ) -> tuple[list[tuple[int, int]], set[int], set[int]]: - """ - Associate detections to trackers based on IOU + """Solve the assignment problem on a similarity matrix. Args: - iou_matrix: IOU cost matrix. - detection_boxes: Detected bounding boxes in the form [x1, y1, x2, y2]. + similarity: Similarity matrix (rows=tracks, cols=detections) + used by the solver for ranking. + min_thresh: Minimum similarity for a valid match. + raw_similarity: Optional unmodified similarity matrix. When + provided, the threshold check uses this matrix instead of + ``similarity`` so that solver-side boosts cannot reject + otherwise valid matches. Returns: - Matched indices, unmatched trackers, unmatched detections. + Matched (row, col) pairs, unmatched row indices, unmatched + col indices. """ - matched_indices = [] - unmatched_trackers = set(range(len(self.trackers))) - unmatched_detections = set(range(len(detection_boxes))) - - if len(self.trackers) > 0 and len(detection_boxes) > 0: - # Find optimal assignment using scipy.optimize.linear_sum_assignment. - # Note that it uses a a modified Jonker-Volgenant algorithm with no - # initialization instead of the Hungarian algorithm as mentioned in the - # SORT paper. - row_indices, col_indices = linear_sum_assignment(iou_matrix, maximize=True) - for row, col in zip(row_indices, col_indices): - if iou_matrix[row, col] >= self.minimum_iou_threshold: - matched_indices.append((row, col)) - unmatched_trackers.remove(row) - unmatched_detections.remove(col) - - return matched_indices, unmatched_trackers, unmatched_detections + n_rows, n_cols = similarity.shape + matched: list[tuple[int, int]] = [] + unmatched_rows = set(range(n_rows)) + unmatched_cols = set(range(n_cols)) + + thresh_matrix = raw_similarity if raw_similarity is not None else similarity + + if n_rows > 0 and n_cols > 0: + row_idx, col_idx = linear_sum_assignment(similarity, maximize=True) + for r, c in zip(row_idx, col_idx): + if thresh_matrix[r, c] >= min_thresh: + matched.append((r, c)) + unmatched_rows.remove(r) + unmatched_cols.remove(c) + + return matched, unmatched_rows, unmatched_cols def _spawn_new_trackers( self, @@ -118,12 +176,61 @@ def _spawn_new_trackers( or confidences[detection_idx] >= self.track_activation_threshold ): self.trackers.append( - SORTKalmanBoxTracker(detection_boxes[detection_idx]) + SORTKalmanBoxTracker( + detection_boxes[detection_idx], + velocity_decay=self.velocity_decay, + q_miss_alpha=self.q_miss_alpha, + p_reset_threshold=self.p_reset_threshold, + oru_threshold=self.oru_threshold, + ) ) + def _build_solver_iou( + self, + raw_iou: np.ndarray, + trackers: list[SORTKalmanBoxTracker], + confidences: np.ndarray | None, + ) -> np.ndarray: + """Apply age discount and confidence boost to the raw similarity matrix. + + Args: + raw_iou: Raw DIoU similarity matrix (tracks x detections). + trackers: Track list whose rows correspond to ``raw_iou``. + confidences: Per-detection confidence scores, or None. + + Returns: + Solver similarity matrix with discounts/boosts applied. + """ + solver_iou = raw_iou + + # Age discount: scale down similarity for lost tracks so the solver + # prefers active tracks over stale predictions. + if self.iou_age_weight > 0 and solver_iou.size > 0: + lost_frames = np.array( + [max(0, t.time_since_update - 1) for t in trackers], + dtype=np.float32, + ) + discount = 1.0 / (1.0 + self.iou_age_weight * lost_frames) + solver_iou = (solver_iou * discount[:, np.newaxis]).astype(np.float32) + + # Confidence boost: scale up solver similarity for higher-confidence + # detections so the assignment prefers confident detections over uncertain + # ones when DIoU values are close. + if ( + self.conf_cost_weight > 0 + and solver_iou.size > 0 + and confidences is not None + ): + conf_boost = 1.0 + self.conf_cost_weight * confidences + solver_iou = (solver_iou * conf_boost[np.newaxis, :]).astype(np.float32) + + return solver_iou + def update(self, detections: sv.Detections) -> sv.Detections: """Update tracker state with new detections and return tracked objects. - Performs Kalman filter prediction, IoU-based association, and initializes + + Performs Kalman filter prediction, optionally two-stage DIoU-based + association (high-confidence then low-confidence), and initializes new tracks for unmatched high-confidence detections. Args: @@ -142,25 +249,64 @@ def update(self, detections: sv.Detections) -> sv.Detections: detection_boxes = ( detections.xyxy if len(detections) > 0 else np.array([]).reshape(0, 4) ) + confidences = detections.confidence for tracker in self.trackers: tracker.predict() - iou_matrix = get_iou_matrix(self.trackers, detection_boxes) - matched_indices, _, unmatched_detections = self._get_associated_indices( - iou_matrix, detection_boxes + # Two-stage split: separate high and low confidence detections. + # When high_conf_det_threshold == 0, all detections are "high" and + # stage 2 is empty — equivalent to single-stage SORT. + use_two_stage = ( + self.high_conf_det_threshold > 0 + and confidences is not None + and len(detection_boxes) > 0 ) - # Update matched trackers and record the det_idx -> tracker mapping - matched_tracker_for_det: dict[int, SORTKalmanBoxTracker] = {} - for row, col in matched_indices: - self.trackers[row].update(detection_boxes[col]) - matched_tracker_for_det[col] = self.trackers[row] + if use_two_stage: + high_mask = confidences >= self.high_conf_det_threshold + high_indices = np.where(high_mask)[0] + low_indices = np.where(~high_mask)[0] + high_boxes = detection_boxes[high_indices] + low_boxes = detection_boxes[low_indices] + high_confs = confidences[high_indices] + else: + high_indices = np.arange(len(detection_boxes)) + high_boxes = detection_boxes + high_confs = confidences + low_indices = np.array([], dtype=int) + low_boxes = np.array([]).reshape(0, 4) - self._spawn_new_trackers( - detections.confidence, detection_boxes, unmatched_detections + # --- Stage 1: match high-confidence detections to all tracks --- + raw_iou = get_iou_matrix(self.trackers, high_boxes) + solver_iou = self._build_solver_iou(raw_iou, self.trackers, high_confs) + + matched_s1, unmatched_tracks_s1, unmatched_high = self._match( + solver_iou, self.minimum_iou_threshold, raw_similarity=raw_iou ) + # Record matched track<->detection mapping (global det indices) + matched_tracker_for_det: dict[int, SORTKalmanBoxTracker] = {} + for row, col in matched_s1: + global_idx = int(high_indices[col]) + self.trackers[row].update(high_boxes[col]) + matched_tracker_for_det[global_idx] = self.trackers[row] + + # --- Stage 2: match low-confidence detections to unmatched tracks --- + if use_two_stage and len(low_boxes) > 0 and len(unmatched_tracks_s1) > 0: + remaining = [self.trackers[i] for i in unmatched_tracks_s1] + raw_iou_s2 = get_iou_matrix(remaining, low_boxes) + # No conf boost or age discount for stage 2 — keep it simple + matched_s2, _, _ = self._match(raw_iou_s2, self.stage2_iou_threshold) + for row, col in matched_s2: + global_idx = int(low_indices[col]) + remaining[row].update(low_boxes[col]) + matched_tracker_for_det[global_idx] = remaining[row] + + # Spawn new tracks from unmatched high-confidence detections only + unmatched_global = {int(high_indices[c]) for c in unmatched_high} + self._spawn_new_trackers(confidences, detection_boxes, unmatched_global) + self.trackers = get_alive_trackers( self.trackers, self.minimum_consecutive_frames, diff --git a/trackers/core/sort/utils.py b/trackers/core/sort/utils.py index 987f90f4..caa8f33c 100644 --- a/trackers/core/sort/utils.py +++ b/trackers/core/sort/utils.py @@ -50,11 +50,67 @@ def get_alive_trackers( return alive_trackers +def _compute_diou_matrix(boxes_a: np.ndarray, boxes_b: np.ndarray) -> np.ndarray: + """Compute Distance IoU (DIoU) between two sets of boxes. + + DIoU = IoU - d^2 / c^2 where d is the Euclidean distance between box + centers and c is the diagonal length of the smallest enclosing box. + Ranges from -1 to 1; penalizes center displacement directly. + + Reference: Zheng et al., "Distance-IoU Loss", AAAI 2020. + + Args: + boxes_a: Array of shape ``(M, 4)`` in ``[x1, y1, x2, y2]`` format. + boxes_b: Array of shape ``(N, 4)`` in ``[x1, y1, x2, y2]`` format. + + Returns: + DIoU matrix of shape ``(M, N)``. + """ + # Intersection coordinates + x1_inter = np.maximum(boxes_a[:, 0:1], boxes_b[:, 0:1].T) + y1_inter = np.maximum(boxes_a[:, 1:2], boxes_b[:, 1:2].T) + x2_inter = np.minimum(boxes_a[:, 2:3], boxes_b[:, 2:3].T) + y2_inter = np.minimum(boxes_a[:, 3:4], boxes_b[:, 3:4].T) + + inter_area = np.maximum(x2_inter - x1_inter, 0) * np.maximum(y2_inter - y1_inter, 0) + + # Areas of individual boxes + area_a = ( + (boxes_a[:, 2] - boxes_a[:, 0]) * (boxes_a[:, 3] - boxes_a[:, 1]) + ).reshape(-1, 1) + area_b = ( + (boxes_b[:, 2] - boxes_b[:, 0]) * (boxes_b[:, 3] - boxes_b[:, 1]) + ).reshape(1, -1) + + union_area = area_a + area_b - inter_area + iou = np.where(union_area > 0, inter_area / union_area, 0.0) + + # Center distance squared + cx_a = ((boxes_a[:, 0] + boxes_a[:, 2]) / 2).reshape(-1, 1) + cy_a = ((boxes_a[:, 1] + boxes_a[:, 3]) / 2).reshape(-1, 1) + cx_b = ((boxes_b[:, 0] + boxes_b[:, 2]) / 2).reshape(1, -1) + cy_b = ((boxes_b[:, 1] + boxes_b[:, 3]) / 2).reshape(1, -1) + d_sq = (cx_a - cx_b) ** 2 + (cy_a - cy_b) ** 2 + + # Enclosing box diagonal squared + x1_c = np.minimum(boxes_a[:, 0:1], boxes_b[:, 0:1].T) + y1_c = np.minimum(boxes_a[:, 1:2], boxes_b[:, 1:2].T) + x2_c = np.maximum(boxes_a[:, 2:3], boxes_b[:, 2:3].T) + y2_c = np.maximum(boxes_a[:, 3:4], boxes_b[:, 3:4].T) + c_sq = (x2_c - x1_c) ** 2 + (y2_c - y1_c) ** 2 + + diou = iou - np.where(c_sq > 0, d_sq / c_sq, 0.0) + + return diou.astype(np.float32) + + def get_iou_matrix( trackers: Sequence[KalmanBoxTrackerType], detection_boxes: np.ndarray ) -> np.ndarray: - """ - Build IOU cost matrix between detections and predicted bounding boxes + """Build DIoU similarity matrix between tracked and detected boxes. + + Uses Distance IoU (DIoU) instead of standard IoU to penalize center + displacement and recover association signal for near-miss pairs. Args: trackers: List of KalmanBoxTracker objects. @@ -62,7 +118,7 @@ def get_iou_matrix( form [x1, y1, x2, y2]. Returns: - IOU cost matrix. + DIoU similarity matrix. """ predicted_boxes = np.array([t.get_state_bbox() for t in trackers]) if len(predicted_boxes) == 0 and len(trackers) > 0: @@ -70,13 +126,77 @@ def get_iou_matrix( predicted_boxes = np.zeros((len(trackers), 4), dtype=np.float32) if len(trackers) > 0 and len(detection_boxes) > 0: - iou_matrix = sv.box_iou_batch(predicted_boxes, detection_boxes) + iou_matrix = _compute_diou_matrix(predicted_boxes, detection_boxes) else: iou_matrix = np.zeros((len(trackers), len(detection_boxes)), dtype=np.float32) return iou_matrix +def interpolate_mot_gaps( + lines: list[str], + max_gap: int = 20, +) -> list[str]: + """Fill short gaps in MOT-format output via linear bbox interpolation. + + For each track that disappears for up to ``max_gap`` consecutive frames + and then reappears, linearly interpolate the bounding box coordinates + between the last observation before the gap and the first observation after. + + Args: + lines: MOT-format lines, each ``"frame,id,x,y,w,h,conf,-1,-1,-1"``. + max_gap: Maximum gap length (in frames) to interpolate. Gaps longer + than this are left unfilled. ``0`` disables interpolation. + + Returns: + Augmented list of MOT-format lines including interpolated entries. + + Examples: + >>> obs = ["1,1,10,20,30,40,0.9,-1,-1,-1", "3,1,16,26,30,40,0.8,-1,-1,-1"] + >>> result = interpolate_mot_gaps(obs, max_gap=2) + >>> any("2,1," in r for r in result) + True + """ + if not lines or max_gap <= 0: + return lines + + tracks: dict[int, list[tuple[int, float, float, float, float, float]]] = {} + for line in lines: + parts = line.split(",") + if len(parts) < 7: + continue + frame = int(parts[0]) + tid = int(parts[1]) + x, y, w, h = float(parts[2]), float(parts[3]), float(parts[4]), float(parts[5]) + conf = float(parts[6]) + tracks.setdefault(tid, []).append((frame, x, y, w, h, conf)) + + for tid in tracks: + tracks[tid].sort(key=lambda t: t[0]) + + interp_lines: list[str] = [] + for tid, obs in tracks.items(): + for i in range(len(obs) - 1): + f1, x1, y1, w1, h1, c1 = obs[i] + f2, x2, y2, w2, h2, c2 = obs[i + 1] + gap = f2 - f1 + if gap <= 1 or gap > max_gap + 1: + continue + for j in range(1, gap): + alpha = j / gap + fx = x1 + alpha * (x2 - x1) + fy = y1 + alpha * (y2 - y1) + fw = w1 + alpha * (w2 - w1) + fh = h1 + alpha * (h2 - h1) + fc = min(c1, c2) + interp_lines.append( + f"{f1 + j},{tid},{fx:.2f},{fy:.2f}," + f"{fw:.2f},{fh:.2f},{fc:.4f},-1,-1,-1" + ) + + return lines + interp_lines + + def update_detections_with_track_ids( trackers: Sequence[KalmanBoxTrackerType], detections: sv.Detections, diff --git a/uv.lock b/uv.lock index 1d639a00..ffd51dde 100644 --- a/uv.lock +++ b/uv.lock @@ -8,15 +8,20 @@ resolution-markers = [ "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux'", "python_full_version == '3.13.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version >= '3.14' and platform_machine != 'aarch64' and 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sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", + "python_full_version == '3.12.*' and sys_platform == 'win32'", + "(python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and sys_platform != 'win32')", "python_full_version == '3.11.*' and sys_platform == 'darwin'", "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'win32'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and 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