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Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition

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Computer Vision – ECCV 2022 Workshops (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13805))

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Abstract

Detection of fights is an important surveillance application in videos. Most existing methods use supervised binary action recognition. Since frame-level annotations are very hard to get for anomaly detection, weakly supervised learning using multiple instance learning is widely used. This paper explores the detection of fights in videos as one special type of anomaly detection and binary action recognition. We use the UBI-Fight and NTU-CCTV-Fight datasets for most of the study since they have frame-level annotations. We find that anomaly detection performs similarly or even better than action recognition. Furthermore, we study to use anomaly detection as a toolbox to generate training datasets for action recognition in an iterative way conditioned on the performance of the anomaly detection. Experiment results should show that we achieve state-of-the-art performance on three fight detection datasets.

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Author information

Authors and Affiliations

  1. Jovision-Deepcam Research Institute, Shenzhen, China

    Weijun Tan & **gfeng Liu

  2. LinkSprite Technology, Longmont, CO, 80503, USA

    Weijun Tan

Authors
  1. Weijun Tan
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  2. **gfeng Liu
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Correspondence to Weijun Tan .

Editor information

Editors and Affiliations

  1. IBM Research AI and MIT-IBM Watson AI Lab, Haifa, Israel

    Leonid Karlinsky

  2. Technion – Israel Institute of Technology, Haifa, Israel

    Tomer Michaeli

  3. Kyoto University, Kyoto, Japan

    Ko Nishino

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Tan, W., Liu, J. (2023). Detection of Fights in Videos: A Comparison Study of Anomaly Detection and Action Recognition. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13805. Springer, Cham. https://doi.org/10.1007/978-3-031-25072-9_45

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  • DOI: https://doi.org/10.1007/978-3-031-25072-9_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-25071-2

  • Online ISBN: 978-3-031-25072-9

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