Benchmarking PySceneDetect

May 28, 2026 · View on GitHub

Benchmarks PySceneDetect's detection accuracy and latency against public shot-boundary-detection corpora. Scoring follows the TRECVID-SBD convention (greedy 1-to-1 nearest-neighbor matching with a configurable frame tolerance for hard cuts; point-in-interval matching for fade transitions; mean absolute frame offset on matched events) so numbers are comparable to published SBD results.

Supported datasets:

BBC Planet Earth: 11 long-form broadcast clips; hard cuts only
AutoShot: Short-form web clips; hard cuts only
ClipShots: Short-form web clips; hard cuts and typed gradual transitions (fades/dissolves)

Usage

# Single detector x single dataset:
python -m benchmark --detector detect-content --dataset BBC

Pass --help for --dataset-root, --backend, --tolerance, and --out options.

Dataset Download

BBC

# annotations
wget -O BBC/fixed.zip https://zenodo.org/records/14873790/files/fixed.zip
unzip BBC/fixed.zip -d BBC
rm -rf BBC/fixed.zip

# videos
wget -O BBC/videos.zip https://zenodo.org/records/14873790/files/videos.zip
unzip BBC/videos.zip -d BBC
rm -rf BBC/videos.zip

AutoShot

Download AutoShot_test.tar.gz from Google Drive.

tar -zxvf AutoShot_test.tar.gz
rm AutoShot_test.tar.gz

ClipShots

ClipShots is gated behind a dataset request form; direct wget-style download links are not published. See the download instructions to obtain the annotations and videos. The expected on-disk layout is:

ClipShots/
  annotations/{train,test,only_gradual}.json
  video_lists/{train,test,only_gradual}.txt
  videos/*.mp4

The loader defaults to the test split (500 videos). The full corpus is ~46 GB.

Set --dataset-root /path/to/datasets to override. The default dataset location assumes they are all placed in the benchmark folder (e.g. benchmark/BBC, benchmark/AutoShot, benchmark/ClipShots).

Results (defaults)

Generated by scripts/benchmark_defaults.sh at tolerance=0 (strict frame-exact matching). Elapsed is mean wall-clock seconds per video.

BBC

Detector	Recall	Precision	F1	Mean s/video
AdaptiveDetector	87.12	96.55	91.59	36.12
ContentDetector	84.70	88.77	86.69	37.02
HashDetector	92.30	75.56	83.10	25.51
HistogramDetector	89.84	72.03	79.96	22.29
ThresholdDetector	0.06	0.70	0.11	16.05

AutoShot

Detector	Recall	Precision	F1	Mean s/video
AdaptiveDetector	70.59	77.46	73.86	3.52
ContentDetector	63.49	76.19	69.26	4.80
HashDetector	56.48	76.11	64.84	4.14
HistogramDetector	63.27	53.23	57.82	3.76
ThresholdDetector	0.75	38.64	1.47	3.28

ClipShots (hard cuts)

Detector	Recall	Precision	F1	Mean s/video
AdaptiveDetector	85.97	41.25	55.75	1.81
ContentDetector	81.93	42.36	55.84	2.52
HashDetector	81.34	30.14	43.98	1.04
HistogramDetector	72.20	11.47	19.80	0.71
ThresholdDetector	0.08	0.58	0.14	0.64

ClipShots (fades)

Detector	Recall	Precision	F1
AdaptiveDetector	13.65	98.12	23.96
ContentDetector	26.03	98.04	41.14
HashDetector	18.77	94.53	31.33
HistogramDetector	69.67	81.99	75.33
ThresholdDetector	5.69	99.24	10.77

Citations

BBC

@InProceedings{bbc_dataset,
  author    = {Lorenzo Baraldi and Costantino Grana and Rita Cucchiara},
  title     = {A Deep Siamese Network for Scene Detection in Broadcast Videos},
  booktitle = {Proceedings of the 23rd ACM International Conference on Multimedia},
  year      = {2015},
}

AutoShot

@InProceedings{autoshot_dataset,
  author    = {Wentao Zhu and Yufang Huang and Xiufeng Xie and Wenxian Liu and Jincan Deng and Debing Zhang and Zhangyang Wang and Ji Liu},
  title     = {AutoShot: A Short Video Dataset and State-of-the-Art Shot Boundary Detection},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  year      = {2023},
}

ClipShots

@InProceedings{clipshots_dataset,
  author    = {Shitao Tang and Litong Feng and Zhanghui Kuang and Yimin Chen and Wei Zhang},
  title     = {Fast Video Shot Transition Localization with Deep Structured Models},
  booktitle = {Asian Conference on Computer Vision (ACCV)},
  year      = {2018},
}