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docs(autotrack): add Journal section + fill SDP benchmark results
- Add Journal › ByteTrack section: 10-row experiment table (kept iterations), collapsed descriptions block, code features table, failed experiments list, key lesson - Fill SDP + autotrack + Optuna row: HOTA 59.092, IDF1 71.993, MOTA 66.977, IDSW 259 --- Co-authored-by: Claude Code <noreply@anthropic.com>
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‎autotrack/README.md‎

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@@ -120,20 +120,20 @@ Published reference points (MOT17-val, FRCNN, IoU-only): SORT ~45–50 (estimate
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Bundled SDP detections; same ground truth as FRCNN. Full 7-sequence eval.
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| Config | Metric | ByteTrack | OC-SORT | SORT |
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| -------------------- | ------ | ----------- | ----------- | ----------- |
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| Defaults | HOTA | 53.941 | 53.351 | 53.217 |
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| | IDF1 | 65.402 | 65.817 | 64.538 |
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| | MOTA | 62.464 | 58.731 | 61.917 |
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| | IDSW | 371 | 283 | 355 |
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| + Optuna (n=500) | HOTA | 56.115 | **57.747** | 56.083 |
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| | IDF1 | 68.077 | 70.330 | 67.517 |
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| | MOTA | 65.602 | 66.215 | 65.283 |
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| | IDSW | 329 | 303 | 326 |
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| + autotrack + Optuna | HOTA | _(pending)_ | _(pending)_ | _(pending)_ |
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| | IDF1 | _(pending)_ | _(pending)_ | _(pending)_ |
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| | MOTA | _(pending)_ | _(pending)_ | _(pending)_ |
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| | IDSW | _(pending)_ | _(pending)_ | _(pending)_ |
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| Config | Metric | ByteTrack | OC-SORT | SORT |
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| -------------------- | ------ | ---------- | ----------- | ----------- |
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| Defaults | HOTA | 53.941 | 53.351 | 53.217 |
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| | IDF1 | 65.402 | 65.817 | 64.538 |
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| | MOTA | 62.464 | 58.731 | 61.917 |
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| | IDSW | 371 | 283 | 355 |
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| + Optuna (n=500) | HOTA | 56.115 | **57.747** | 56.083 |
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| | IDF1 | 68.077 | 70.330 | 67.517 |
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| | MOTA | 65.602 | 66.215 | 65.283 |
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| | IDSW | 329 | 303 | 326 |
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| + autotrack + Optuna | HOTA | **59.092** | _(pending)_ | _(pending)_ |
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| | IDF1 | **71.993** | _(pending)_ | _(pending)_ |
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| | MOTA | **66.977** | _(pending)_ | _(pending)_ |
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| | IDSW | **259** | _(pending)_ | _(pending)_ |
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> 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.
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</details>
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## Journal
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### ByteTrack — Phase 2 Campaign (MOT17-val SDP, 20 iterations)
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**Period**: 2026-04-02 → 2026-04-04 | **Baseline**: HOTA = 56.003 | **Final**: HOTA = 59.092 (+5.51%, +3.089 pts) | **Best commit**: `9f45bda`
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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.
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#### Positive experiments
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| Iter | Change | HOTA before → after | Δ pts | Δ % | IDSW |
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| ---- | --------------------------------- | ------------------- | ------ | ------ | --------- |
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| i1 | ORU velocity re-estimation | 56.003 → 56.063 | +0.060 | +0.11% | 323 → 314 |
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| i2b | Separate stage-2 IoU threshold | 56.063 → 56.196 | +0.133 | +0.24% | 314 → 320 |
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| i5 | Stage-1 age discount | 56.196 → 56.781 | +0.585 | +1.04% | — |
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| i10 | 1st calibration wave | 56.781 → 57.424 | +0.643 | +1.13% | — |
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| i11 | oru_threshold 2 → 5 | 57.424 → 57.813 | +0.389 | +0.68% | — |
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| i12 | 2nd calibration wave | 57.813 → 58.753 | +0.940 | +1.62% | → 297 |
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| i16 | 3rd calibration wave | 58.753 → 58.862 | +0.109 | +0.19% | 297 → 269 |
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| i17 | stage2_min_updates search cap fix | 58.862 → 58.961 | +0.099 | +0.17% | 269 → 266 |
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| i18 | Micro-calibration (wave 1) | 58.961 → 59.031 | +0.070 | +0.12% | 266 → 262 |
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| i19 | Micro-calibration (wave 2) | 59.031 → 59.092 | +0.061 | +0.10% | 262 → 259 |
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<details>
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<summary><strong>Experiment descriptions</strong></summary>
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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].
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- **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%.
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- **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.
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</details>
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#### Code features added
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All six features are in `trackers/core/bytetrack/tracker.py` and wired through `optimize_tracking.py`:
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| Parameter | Default | What it does |
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| ---------------------- | ------- | ----------------------------------------------------------------------------------- |
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| `stage2_iou_threshold` | 0.2999 | Independent IoU gate for stage-2 low-confidence detection recovery |
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| `iou_age_weight` | 0.07197 | Age discount factor for stage-1 Hungarian assignment cost |
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| `conf_cost_weight` | 0.1696 | Confidence boost multiplier in stage-1 assignment, biases toward certain detections |
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| `stage2_min_updates` | 12 | Minimum track age (successful updates) required to enter stage-2 |
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| `giou_blend` | 0.42 | Blend weight between standard IoU and GIoU in stage-1 cost matrix |
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| `oru_threshold` | 0 | Minimum occlusion length (frames) to trigger ORU velocity replay |
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#### What failed (reverted)
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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).
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#### Key lesson
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**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.
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---
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## Target analysis
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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.

‎autotrack/best_config.json‎

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}
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},
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"sdp": {
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"hota": 59.092,
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"hota": 59.13051418925587,
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"config": {
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"lost_track_buffer": 57,
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"track_activation_threshold": 0.3328,
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"lost_track_buffer": 51,
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"track_activation_threshold": 0.5114905649202629,
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"minimum_consecutive_frames": 1,
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"minimum_iou_threshold": 0.146,
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"stage2_iou_threshold": 0.2999,
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"iou_age_weight": 0.07197,
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"high_conf_det_threshold": 0.7952,
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"q_scale": 0.003,
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"r_scale": 0.5762,
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"p_scale": 2.5,
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"velocity_decay": 0.82,
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"q_miss_alpha": 0.7961,
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"max_interpolation_gap": 48,
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"p_reset_threshold": 12,
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"oru_threshold": 0,
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"conf_cost_weight": 0.1696,
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"stage2_min_updates": 12,
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"giou_blend": 0.42
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"minimum_iou_threshold": 0.052265500917315036,
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"stage2_iou_threshold": 0.29979835373383396,
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"iou_age_weight": 0.05005507049974921,
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"high_conf_det_threshold": 0.6487131729567451,
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"q_scale": 0.0017128495311350704,
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"r_scale": 0.9309506015595449,
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"p_scale": 2.902648663733205,
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"velocity_decay": 0.964229114603559,
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"q_miss_alpha": 0.8418166381682424,
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"max_interpolation_gap": 45,
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"p_reset_threshold": 26,
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"oru_threshold": 13,
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"conf_cost_weight": 0.06609412915120773,
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"stage2_min_updates": 13,
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"giou_blend": 0.28382437286975765
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}
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},
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"yoloworld": {

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