Target analysis based anomaly detection in surveillance videos

Jie Zhang; Weihai Li

doi:10.1117/12.2573117

12 June 2020 Target analysis based anomaly detection in surveillance videos

Jie Zhang, Weihai Li

Proceedings Volume 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020); 1151908 (2020) https://doi.org/10.1117/12.2573117
Event: Twelfth International Conference on Digital Image Processing, 2020, Osaka, Japan

Abstract

Abnormal behavior detection in surveillance video is a pivotal part of the intelligent city. Most of the existing methods only consider how to detect anomalies, with less considering to explain the reason of the anomalies. In this work, we investigate an orthogonal perspective based on the reason of these abnormal behaviors. We propose a multivariate fusion method that analyzes each target through three branches: object, action and motion. The object branch focuses on the appearance information, the motion branch focuses on the distribution of the motion features, and the action branch focuses on the action category of the target. The information that these branches focus on is different, and they can complement each other and jointly detect abnormal behavior. The final abnormal score can then be obtained by combining the abnormal scores of the three branches. In the action branch, we also propose an action recognition module using inter-frame information to solve the multi-target and multi-action recognition in the surveillance video, which is not utilized before in the anomaly detection field. The proposed method outperforms the state-of-the-art methods and also can explain why the target is detected as an anomaly.

Citation Download Citation

Jie Zhang and Weihai Li "Target analysis based anomaly detection in surveillance videos", Proc. SPIE 11519, Twelfth International Conference on Digital Image Processing (ICDIP 2020), 1151908 (12 June 2020); https://doi.org/10.1117/12.2573117

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Video surveillance

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