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Single Scene Video Anomaly Detection Metrics

Project description

Single-Scene Video Anomaly Detection Metrics

This project contains evaluation protocol (metrics) for benchmarking single-scene video anomaly detection.

Evaluation Protocol

This is an unofficial implementation of Sec. 2.2 of A Survey of Single-Scene Video Anomaly Detection.


This metrics is available via PyPI.

pip install py-ssvad-metrics


  1. Prepare ground-truth JSON file and prediction JSON file. Examples are in the tests folder.

  2. For UCSD Pedestrian 1 and 2 datasets, CUHK Avenue dataset, and Street Scene dataset, we provided scripts for converting ground-truth annotation files from Street Scene dataset. Download link is provided in the paper [].

  3. Example usage for single groundtruth and prediction file pair:

    import ssvad_metrics
    result = ssvad_metrics.metrics.evaluate(
  4. Example usage for multiple groundtruth and prediction file pairs:

    import ssvad_metrics
    result = ssvad_metrics.metrics.accumulated_evaluate(


  1. B. Ramachandra, M. Jones and R. R. Vatsavai, "A Survey of Single-Scene Video Anomaly Detection," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2020.3040591.


Copyright PT Qlue Performa Indonesia 2021 All Rights Reserved


Feel free to contribute for improvements.

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