TR2018-188

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


    •  Jones, M.J., Ramachandra, B., "Street Scene: A new dataset and evaluation protocol for video anomaly detection", arXiv, January 2019.
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      • @techreport{MERL_TR2018-188,
      • author = {Jones, M.J. and Ramachandra, B.},
      • title = {Street Scene: A new dataset and evaluation protocol for video anomaly detection},
      • institution = {MERL - Mitsubishi Electric Research Laboratories},
      • address = {Cambridge, MA 02139},
      • number = {TR2018-188},
      • month = jan,
      • year = 2019,
      • url = {http://www.merl.com/publications/TR2018-188/}
      • }
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  • Research Areas:

    Artificial Intelligence, Computer Vision, Machine Learning

  • Research Highlights:
    Research Highlight
    Video Anomaly Detection

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 state-of-the-art algorithms from the literature.