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Scale-Aware Spatio-Temporal Relation Learning for Video Anomaly Detection

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Book cover Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13664))

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Abstract

Recent progress in video anomaly detection (VAD) has shown that feature discrimination is the key to effectively distinguishing anomalies from normal events. We observe that many anomalous events occur in limited local regions, and the severe background noise increases the difficulty of feature learning. In this paper, we propose a scale-aware weakly supervised learning approach to capture local and salient anomalous patterns from the background, using only coarse video-level labels as supervision. We achieve this by segmenting frames into non-overlapping patches and then capturing inconsistencies among different regions through our patch spatial relation (PSR) module, which consists of self-attention mechanisms and dilated convolutions. To address the scale variation of anomalies and enhance the robustness of our method, a multi-scale patch aggregation method is further introduced to enable local-to-global spatial perception by merging features of patches with different scales. Considering the importance of temporal cues, we extend the relation modeling from the spatial domain to the spatio-temporal domain with the help of the existing video temporal relation network to effectively encode the spatio-temporal dynamics in the video. Experimental results show that our proposed method achieves new state-of-the-art performance on UCF-Crime and ShanghaiTech benchmarks. Code are available at https://github.com/nutuniv/SSRL.

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Author information

Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoqiu Li

  2. SenseTime Research, Shanghai, China

    Guanxiong Cai, Xingyu Zeng & Rui Zhao

  3. Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China

    Rui Zhao

Authors
  1. Guoqiu Li
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  2. Guanxiong Cai
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  3. Xingyu Zeng
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  4. Rui Zhao
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Corresponding author

Correspondence to Xingyu Zeng .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Li, G., Cai, G., Zeng, X., Zhao, R. (2022). Scale-Aware Spatio-Temporal Relation Learning for Video Anomaly Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13664. Springer, Cham. https://doi.org/10.1007/978-3-031-19772-7_20

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  • DOI: https://doi.org/10.1007/978-3-031-19772-7_20

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