Sensitivity
High sensitivity reacts to smaller visual changes and usually reports more candidates. Low sensitivity keeps only stronger changes but may miss subtle cuts.
Detect Scene Changes in Video
Find likely visual cut points, review scene thumbnails and timestamps, and export a structured scene list for editing, chapters or analysis.
Visual-change analysisScene timelineJSON or CSV export
Read the full guide11. Select videoSelect a file or direct URL
2AI DetectionSample frames and detect cuts
3ResultsPreview and export scenes
1. Select video
Supported formats: MP4, WebM, MOV, MKV
Drop a video here
Maximum file size: 2 GB
Detection settings
Adjust sensitivity before generating the scene list.
Medium
LowMediumHigh
Analysis preview
Scrub the video and review detected cut points.
No videos have been added yet.
The preview and scene timeline will appear here.
No scene boundary has been detected yet.
00:0000:00
Detection summary
- Scenes detected
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- Duration
- 00:00:00
- Average scene length
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- Method
- AI + Visual
- Status
- Waiting
Detect scenes
Detected scenes
Click a scene to inspect timing and export that cut marker.
#ThumbnailStartEndDurationScore
No scene boundary has been detected yet.
Selected scene
Select a detected scene
Preview appears after detection
Start
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End
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Duration
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If verified, local files and model inference run in the browser. Model assets may still be downloaded, and URL media still comes from the remote host. Disclose both network paths and any cache behavior instead of using an absolute “offline” claim.
What does AI scene detection find?
It compares visual information across sampled frames to identify likely cuts or strong transitions. It returns candidate boundaries, not a human-level understanding of the story, topic or meaning of each scene.
Scene-change detection finds visual boundaries, not meaning
The analyzer samples frames and measures visual differences to propose where one shot or scene may end and another begin. A result can include false positives from flashes, camera motion or graphics, and false negatives from gradual transitions. Human review remains necessary.
Who uses scene detection?
Editors, archivists, QA engineers, educators, researchers and developers use scene timestamps to navigate long media, build chapters, select thumbnails, prepare rough cuts and test video-analysis workflows.
Scene detection use cases
- Create candidate chapter markers for a long recording.
- Find likely cut points before manual editing.
- Generate representative thumbnails from each visual segment.
- Review the structure of a lecture, interview or presentation.
- Export timestamps for downstream scripts or media pipelines.
- Compare detector settings during QA or model evaluation.
How to detect scenes
Choose a local video or a direct URL the browser can access.
Start with medium sensitivity and a realistic minimum scene length.
Run the analysis and wait while frames are sampled and compared.
Review thumbnails, timestamps and change scores; remove false boundaries or add missing ones when the UI supports it.
Export the scene list in a supported structured format, or export clips only when the current media engine implements that capability.
How detection settings affect results
Minimum scene length
Prevents rapid changes from creating impractically short scenes. Choose a value based on the content pace, not a universal default.
Sampling rate
A detector that checks more frames can localize short events better but takes longer and uses more compute. The UI must reflect the actual sampling implementation.
Change score and confidence
These are algorithmic signals, not proof of a true editorial scene. Explain how the current model calculates or normalizes them if that information exists in source.
Export format
JSON is suitable for applications, CSV for spreadsheets, and Markdown for human-readable review. Export only fields actually available in the result.
Scene-change detection is not semantic video understanding
A visual detector can identify cuts, fades or strong appearance changes. It does not automatically know that a scene is “an interview,” “a product demo” or “a conclusion” unless a separate verified semantic model performs that task. Keep page claims limited to the implemented analysis.
How analysis is processed
If verified, local files and model inference run in the browser. Model assets may still be downloaded, and URL media still comes from the remote host. Disclose both network paths and any cache behavior instead of using an absolute “offline” claim.
Scene detection limitations
- Flashes, fast camera movement, slide changes and large overlays can trigger false positives.
- Slow dissolves, similar-looking cuts and dark footage can be missed.
- Timestamp precision depends on frame sampling, decoding and source time bases.
- Long or high-resolution videos can take significant time and memory.
- The detector does not confirm narrative meaning or legal clip boundaries.
Common detection problems
Too many scenes
Lower sensitivity, increase minimum scene length or remove boundaries caused by flashes and graphics.
Important cuts are missing
Increase sensitivity, reduce minimum length or add a boundary manually if the UI supports it.
Timestamps are slightly late or early
Sampling and keyframe seeking can shift boundaries. Inspect frames around the reported time before cutting.
Analysis is slow
Use a shorter range, lower-resolution proxy or less frequent sampling when supported, and close memory-heavy tabs.
AI scene detection FAQ
What is a scene change?
It is a point where the visual content changes enough to suggest a new shot or segment, such as a hard cut, fade or major layout change.
Does the AI understand what is happening in each scene?
Not necessarily. The current page must describe the implemented model. If it only measures visual differences, it detects boundaries without understanding narrative meaning.
What sensitivity should I choose?
Start with Medium. Use High for subtle or fast edits and Low when flashes or motion create too many false candidates.
What does minimum scene length do?
It suppresses boundaries that would create segments shorter than the selected duration.
Are the detected timestamps exact?
They are candidates based on sampling and decoding. Review nearby frames before using them for precise cuts.
Can scene detection miss a transition?
Yes. Similar-looking cuts, gradual dissolves, darkness and limited sampling can create false negatives.
Can it create clips automatically?
Only when the current implementation includes a compatible clip-export pipeline. Timestamp export and media encoding are separate capabilities.
Can I export JSON or CSV?
Yes when those options are implemented. The exported schema should document timestamp units, score scale and source duration.
Are local videos uploaded?
Only claim local inference when source code confirms it. Model files may be fetched, and URL videos are retrieved from their host.
How is this different from thumbnail generation?
Scene detection finds candidate boundaries; the thumbnail generator extracts frames at chosen times or intervals. They can be used together.
Why did a flash create a scene?
A flash can cause a large frame difference that resembles a cut. Reduce sensitivity or mark that boundary as a false positive.
Should I trust the result without reviewing it?
No. Treat it as an analysis aid and review boundaries, especially before editing, publishing or automated processing.
Related analysis and editing tools
Video Thumbnail GeneratorExtract representative frames from detected scene timestamps.Video TrimmerCut a reviewed range after confirming boundaries.Split VideoCreate multiple segments from approved markers when supported.Metadata ExtractionInspect source duration, frame rate, codecs and time-base clues before analysis.