py-ssvad-metrics 0.2.0

Single Scene Video Anomaly Detection Metrics

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.

Installation

This metrics is available via PyPI.

pip install py-ssvad-metrics

Usage

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 [http://www.merl.com/demos/video-anomaly-detection].

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

   import ssvad_metrics
   result = ssvad_metrics.metrics.evaluate(
       "tests/gt_examples/Test001_gt.json",
       "tests/pred_examples/Test001_pred.json")
   

4. Example usage for multiple groundtruth and prediction file pairs:

   import ssvad_metrics
   result = ssvad_metrics.metrics.accumulated_evaluate(
       "tests/gt_examples",
       "tests/pred_examples",
       gt_name_suffix="_gt",
       pred_name_suffix="_pred")
   

References

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

Copyright PT Qlue Performa Indonesia 2021 All Rights Reserved

Contributing

Feel free to contribute for improvements.