TR2020-017

Street Scene: A new dataset and evaluation protocol for video anomaly detection


    •  Ramachandra, B., Jones, M.J., "Street Scene: A new dataset and evaluation protocol for video anomaly detection", IEEE Winter Conference on Applications of Computer Vision (WACV), DOI: 10.1109/​WACV45572.2020.9093457, February 2020, pp. 2569-2578.
      BibTeX TR2020-017 PDF Data
      • @inproceedings{Jones2020feb2,
      • author = {Ramachandra, Bharathkumar and Jones, Michael J.},
      • title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
      • booktitle = {IEEE Winter Conference on Applications of Computer Vision (WACV)},
      • year = 2020,
      • pages = {2569--2578},
      • month = feb,
      • doi = {10.1109/WACV45572.2020.9093457},
      • url = {https://www.merl.com/publications/TR2020-017}
      • }
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  • Research Area:

    Computer Vision

Abstract:

Progress in video anomaly detection research is currently slowed by small datasets that lack a wide variety of activities as well as flawed evaluation criteria. This paper aims to help move this research effort forward by introducing a large and varied new dataset called Street Scene, as well as two new evaluation criteria that provide a better estimate of how an algorithm will perform in practice. In addition to the new dataset and evaluation criteria, we present two variations of a novel baseline video anomaly detection algorithm and show they are much more accurate on Street Scene than two well known algorithms from the literature.

 

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