# The learned scene-boundary detector

Presence uses **flood-fill**: an actor seen once inside a shot is reported for the
whole shot (`[prev_boundary, next_boundary]`). That only works if the boundaries
are good. This page is the story of getting them good — a learned scene-boundary
detector that lifts per-second actor-presence F1 from **62.6% to 74.9%** across
the nine-film X-Ray benchmark, and fixes the film where naive flood-fill was
actively harmful.

That 74.9% is the **leave-one-out** figure: each film is scored by a detector
trained on the *other eight*, so no film's presence is measured with a detector
that ever saw it. It is the honest generalisation number, and it is only ~1 point
below the all-nine-trained model (75.8%) — the detector barely overfits.

## Why the old cut detector wasn't enough

The always-on boundary source was the grayscale histogram-correlation cut detector
(`camera_position_change_detector`): mark a cut when the frame-to-frame grayscale
histogram correlation drops below 0.70. It is cheap and it fires on obvious hard
cuts, but on a low-contrast, uniformly-graded film it is nearly blind. On
**Scarface** it fired **once in 10,204 frames**. Flood-fill then snapped every
actor across essentially the whole film:

| Scarface | precision | recall |
| -------- | --------- | ------ |
| flood + grayscale cuts | **26%** | 95% |
| track-extent (no flood) | 92% | 45% |

That single failure is what motivated everything below: flood-fill needs a
boundary source that works regardless of grade.

## What we are detecting, and why it is hard

The training target is **Amazon X-Ray scene boundaries** (`scenes.csv`). These are
*narrative* scenes — a new location or beat in the story — not shot cuts. There
are only ~20–60 of them per film (median scene ~170 s), and many transition
*within* continuous visual style and continuous audio. So the signal is sparse and
often genuinely faint: a boundary detector working from audio-visual features can
never recall a narrative cut that has no audio-visual signature.

This shapes every result: absolute boundary-F1 is modest by construction. What
matters is the **downstream** number — does snapping flood-fill to these
boundaries name the right actors — and there the gain is large.

## The features (what worked, measured)

Everything is per second, aligned to the 1-fps presence grid.

- **Delta histograms, not raw histograms.** The raw RGB histogram encodes what a
  frame *looks like*, not that it *changed* — measured boundary separability ~1.4×.
  The **symmetric histogram delta** `|hist(t+k) − hist(t−k)|` separates boundaries
  **4–5×**. Leading with deltas (k = 1,2,4,8 s) and dropping the raw histogram was
  the single biggest feature win (LSTM F1 7.5% → 10.8%).
- **A multi-scale "ramp" bank.** Antisymmetric matched filters at half-widths
  H = 2,4,6,8,10 s; the model weights the scales. Different films' boundaries peak
  at different widths.
- **A time-since-last-boundary "debounce" clock**, scaled by the corpus mean scene
  length (~205 s), encoding that scenes don't restart moments apart.
- **Audio log-PSD** (per-second, 4 s window, ~57 log-frequency bins). Measured
  weak on its own — a standalone audio cutter scored only 3–6% held-out F1, because
  narrative boundaries usually have continuous audio — but it is complementary on
  the films where video is weak (Downton, Sound of Metal), so it is included and
  the model uses it where it helps.

![Detector development at strict ±2 s tolerance, and where the shipped detector landed at the ±20 s tolerance the pipeline uses](assets/images/scene_detector_evolution.png)

The left panel is the *feature* development, scored at a strict ±2 s tolerance so
each change is visible — this is where "delta beats raw histogram" was measured, not
the shipped tolerance. The right panel is the shipped detector at the ±20 s
tolerance the pipeline actually uses (see below). The two panels are on different
tolerances by design and must not be read as one curve.

Dead ends, all measured and discarded: audio-only detection; raw
histograms/PSDs as input; a two-tower BiLSTM (no better than the tree, far slower);
larger FFT windows / more frequency bins (worse — boundaries are short events);
and TransNetV2 (a Conv3D net that will not co-reside with the ROCm/VAAPI stack).

## The model

- **XGBoost regressor** over a ±3 s window of the features above, predicting a
  **soft Gaussian proximity-to-boundary target** (`exp(-(d/σ)²)`, σ = 10 s).
  Regression to a soft target — rather than a hard 0/1 label — stops a near-miss
  from being trained as a hard negative, and yields a smooth score whose **peaks**
  are the boundaries.
- **Per-film knee threshold.** The predicted peak heights form a
  convex-decreasing curve; the knee (max drop below the endpoints' chord) is where
  real boundaries give way to noise. Selecting at the knee **self-calibrates the
  boundary count** to roughly the true scene count, per film, with no global
  threshold that would be wrong for every grade.
- **Trained on all nine films** for the shipped model. Keeping the low-contrast
  grades (Café Society, Scarface) in training matters most: on its own training
  films the shipped model reaches **72.9% macro boundary-F1** (per-film 51–86%),
  versus **29.8%** for the grayscale baseline on the same films.

Boundary detection, held out (leave-one-out, ±20 s tolerance — appropriate given
~170 s scenes): **44.1% macro F1, versus 29.8% for the grayscale baseline** — the
honest generalisation number, each film scored by a detector trained on the other
eight. Even the low-contrast grades generalise (Scarface held out 32%, Café Society
51%), where the grayscale detector scores 0% and 31%. The absolute number is capped
by the narrative-vs-audiovisual mismatch above — many boundaries have no
audio-visual signature at all — so the point is the downstream effect, below.

| boundary-F1 @±20 s | grayscale | learned (LOO) | learned (train-all) |
| ------------------ | --------: | ------------: | ------------------: |
| macro over 9 films | 29.8% | **44.1%** | 72.9% |

## The result that matters: actor presence

Per-second X-Ray presence F1, macro over the nine films, at the shipped presence
config. The learned column is **leave-one-out** — each film scored by a detector
trained on the other eight:

| boundary source for flood-fill | presence F1 |
| ------------------------------ | ----------- |
| track-extent (flood off) | 62.6% |
| flood + grayscale cuts | 64.0% |
| **flood + learned detector (LOO)** | **74.9%** |

![Macro presence F1 by flood-fill boundary source](assets/images/scene_presence_macro.png)

**+12.3 points over track-extent, +10.9 over the grayscale-cut flood, and it
improves every one of the nine films — under honest leave-one-out.** Per film:

![Per-film presence F1 by boundary source](assets/images/scene_presence_by_source.png)

| film | track-extent | flood+grayscale | flood+learned (LOO) |
| ---- | -----------: | --------------: | ------------------: |
| Benny & Joon | 77.3 | 80.2 | 78.2 |
| Café Society | 59.1 | 62.2 | 69.8 |
| Downton Abbey | 41.0 | 51.8 | **78.6** |
| Lord of War | 74.8 | 77.1 | 77.8 |
| Lovelace | 70.3 | 74.0 | 78.2 |
| The Many Saints of Newark | 37.5 | 43.9 | 53.4 |
| Scarface | 62.6 | **40.9** | **74.9** |
| Sound of Metal | 75.0 | 78.1 | 86.8 |
| Valerian | 65.6 | 67.7 | 76.2 |

The two headline cases:

- **Scarface**: the grayscale-cut flood *breaks* it (62.6 → 40.9), because it
  detects one cut in the whole film. The learned detector — **on a film it never
  trained on** — takes it to **74.9%**. This is the strongest evidence the
  detector generalises: it fixes the exact failure that motivated it, held out.
- **Downton Abbey**: 41.0 (track-extent) → 51.8 (grayscale) → **78.6** — a
  +37-point swing on the hardest film.

Naive flood-fill barely beat doing nothing (64% vs 62%) and broke a film. With a
real boundary detector, flood-fill is decisively the right mode.

### What the frames look like

`scripts/optimizer/dump_error_frames.py` pulls representative seconds and draws
each face box coloured against X-Ray's scene cast: **green** = true positive (a
name X-Ray also credits to this scene), **red** = false positive (a name X-Ray
does *not* credit here — the real error), **orange** = an unknown detection. Cast
X-Ray lists as present but for whom no face was detected — the structural
false-negatives a face pipeline can never box — are listed as a **blue** panel.

![A correctly identified second: green true-positive boxes](assets/images/scarface_tp_example.jpg)

Above: three faces named correctly (green). Below: the face-vs-scene-cast tension
made visual — the one visible face is confidently named (here it is a red
false-positive, a lead X-Ray did not credit to this exact scene), while six
credited cast members are off-camera with no face to detect (blue). This is why
recall against X-Ray has a structural ceiling, not a fixable bug.

![A false-positive box (red) with off-screen cast listed (blue)](assets/images/scarface_fn_fp_example.jpg)

## In the pipeline

Boundary detection is a **post-EOF step**, like flood-fill itself: the per-film
knee needs every peak, so it can only run once the whole film is seen. The
`camera_position_change_detector` stamps a per-frame RGB histogram onto each frame;
it rides through to the result sink; at end-of-stream the sink runs the detector
over the collected histograms plus the movie's audio log-PSD and snaps the
presence windows to the result. Enable it with:

```bash
scene_analyze --movie <file> --gallery <gallery.h5> \
    --scene-xgb-model models/scene_boundary_xgb.json
```

Inference is real XGBoost, built into the binary via CMake (`SAE_SCENE_XGB`); the
audio log-PSD uses FFTW + the existing FFmpeg decode. To keep training and
inference on one feature implementation, the shipped model is **trained on the
C++-extracted features** (`scene_features_dump` → `train_xgb_cpp.py`) rather than a
re-implementation in Python — parity by construction. Verified end to end through
`scene_analyze` on a movie file and through the Jellyfin work-queue worker.

## Reproduce

```bash
# per-second audio log-PSD for each film
.venv-rocm/bin/python scripts/scene_detector/extract_audio_features.py \
    --manifest experiments/manifests/films_LVFace_opencv5.json

# C++ feature matrices (same features training and inference share)
build/scene_features_dump <dump.h5> <movie> <features.h5>

# train the shipped model on all nine films
.venv-rocm/bin/python scripts/scene_detector/train_xgb_cpp.py --train-all

# downstream A/B (track-extent vs flood+grayscale vs flood+learned)
scripts/scene_detector/downstream_presence.py
```