How Claude-Video Uses Scene-Change Detection to Select Representative Frames

Claude-Video relies on FFmpeg's built-in scene filter to detect shot boundaries, extracting the first frame of each detected scene using a configurable threshold of 0.20.

The bradautomates/claude-video repository implements intelligent frame extraction by leveraging FFmpeg's scene-change detection capabilities. This approach identifies distinct visual shots within video content, ensuring that extracted frames represent meaningful transitions rather than arbitrary intervals. Understanding this scene-change detection method reveals how the system balances computational efficiency with content-aware sampling.

FFmpeg Scene Filter as the Detection Engine

The core detection mechanism utilizes FFmpeg's scene video filter, which computes the per-pixel difference between consecutive frames and normalizes the result to a value between 0 and 1. When this scene score exceeds the defined threshold, FFmpeg classifies the frame as the start of a new scene or shot boundary.

Implementation Details in skills/watch/scripts/frames.py

Scene Threshold Configuration

The detection sensitivity is controlled by the SCENE_THRESHOLD constant, defined as 0.20 in skills/watch/scripts/frames.py. This value determines how much visual change must occur between frames to trigger a scene detection event. A lower threshold increases sensitivity, capturing subtle transitions, while a higher threshold only registers significant visual changes.

Filter Chain Construction

When processing videos, the script constructs an FFmpeg video-filter chain that combines two selection criteria:

1. The very first frame of the video (eq(n\,0))
2. Any frame where the scene score exceeds the threshold (gt(scene, threshold))

The complete filter expression appears as:

select='eq(n\,0)+gt(scene\,{threshold})'

This ensures capture of the opening frame plus every detected scene cut.

Frame Processing Pipeline

After scene detection, the pipeline applies additional processing:

• Scaling: The _scale_filter function scales output to a configurable resolution (default 512px)
• Timestamp extraction: FFmpeg's showinfo output is parsed using the SHOWINFO_TS_RE regex pattern to associate each extracted JPEG with its precise source time in seconds
• Post-processing: The resulting candidate list undergoes deduplication and even-sampling adjustments within the same module

Practical Usage Example

You can programmatically extract scene-based frames using the extract_scene_candidates function:

from pathlib import Path
from skills.watch.scripts.frames import extract_scene_candidates

video = "example.mp4"
out_dir = Path("frames")
candidates = extract_scene_candidates(
    video_path=video,
    out_dir=out_dir,
    resolution=512,
    max_frames=100,           # optional cap; None = uncapped

    start_seconds=None,
    end_seconds=None,
    threshold=0.20,           # default scene-change sensitivity

)

for f in candidates:
    print(f["index"], f["timestamp_seconds"], f["reason"])

When too few scene changes are detected, the system automatically falls back to uniform sampling to ensure adequate frame coverage.

Summary

• FFmpeg scene filter drives the detection logic by calculating normalized per-pixel differences between consecutive frames
• Threshold-based selection uses a default value of 0.20 defined in skills/watch/scripts/frames.py to identify shot boundaries
• Filter syntax combines initial frame capture with scene-cut detection via select='eq(n\,0)+gt(scene\,{threshold})'
• Fallback mechanism ensures uniform sampling when scene changes are insufficient for the requested frame count
• Timestamp tracking maintains precise temporal metadata for each extracted frame through FFmpeg's showinfo output

Frequently Asked Questions

What specific algorithm does Claude-Video use for scene-change detection?

Claude-Video uses FFmpeg's built-in scene filter, which calculates the per-pixel difference between the current frame and the previous one, normalizing the result to a value between 0 and 1. Frames exceeding the threshold are treated as scene cuts.

How can I adjust the sensitivity of scene-change detection?

Modify the threshold parameter when calling extract_scene_candidates() or adjust the SCENE_THRESHOLD constant in skills/watch/scripts/frames.py. The default value of 0.20 provides balanced detection; lower values capture subtle transitions while higher values require significant visual changes.

What happens when a video contains no scene changes?

If the scene-change detection yields too few frames, the system automatically falls back to uniform sampling. This behavior is implemented in skills/watch/scripts/frames.py to ensure the extraction process always returns a usable set of representative frames.

Does the method capture the first frame of every scene or all frames within a scene?

The method captures only the first frame of each detected scene (plus the video's initial frame). The FFmpeg select filter triggers once when the scene score exceeds the threshold, marking the boundary transition, rather than capturing multiple frames within a single continuous shot.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}