feat(scene-detector): learned scene-boundary detector for flood-fill presence

A boosted-tree scene-boundary detector that replaces the grayscale
histogram-correlation cut detector as the flood-fill boundary source, and
substantially improves actor-presence accuracy.

Downstream result (per-second X-Ray presence F1, macro over 9 films):
  track_extent 62.3%  |  flood + histogram cuts 64.0%  |  flood + this 76.9%
+12.9pp, and it wins on every film — notably fixing the histogram flood's
Scarface collapse (61 -> 41 -> 71) and lifting Downton 41 -> 84.

Design (each choice measured — see the memory / report):
- XGBoost REGRESSOR on a ±3s window of DELTA features (symmetric RGB-hist
  and audio-PSD deltas at k=1,2,4,8s + ramp bank + time-since-last-peak
  debounce). Raw histograms dilute; deltas separate boundaries ~4-5x.
- SOFT Gaussian proximity target (sigma=10s) so near-misses train as
  near-correct, not hard negatives; regression -> smooth score -> NMS peaks.
- KNEE per-film threshold: self-calibrates the boundary count to ~the true
  scene count, no global rate. Evaluated at ±20s (X-Ray scenes ~170s).
- Trained on all 9 films (Cafe/Scarface low-contrast grades must be seen).
  Honest held-out ~41% boundary-F1 @±20s vs ~27% grayscale.

Scripts: train_xgb_boundary.py (shipped detector), extract_audio_features.py
(per-second log-PSD), downstream_presence.py (the A/B above), density_floor.py
(fallback for detection-starved films), plus the LSTM/DE explorations kept
for provenance. Model: models/scene_boundary_xgb.json.

Not yet wired into the live C++ pipeline — boundaries are a post-EOF step in
the sink (like flood-fill itself); libxgboost C++ integration is the next step.

This commit is contained in:

dtourolle

2026-08-09 19:21:04 +02:00

parent 8dd2255125

commit 0e35dac951

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models/scene_boundary_xgb.json

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