2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDMLR-APPL-IVASR-2.6
Paper Title MULTI-SCALE BACKGROUND SUPPRESSION ANOMALY DETECTION IN SURVEILLANCE VIDEOS
Authors Zhen Yang, Yuanfang Guo, Jinjie Wei, Beihang University, China; Xiuguo Bao, National Computer Network Emergency Response Technical Team / Coordination Center of China, China; Di Huang, Beihang University, China
SessionMLR-APPL-IVASR-2: Machine learning for image and video analysis, synthesis, and retrieval 2
LocationArea D
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Applications of Machine Learning: Machine learning for image & video analysis, synthesis, and retrieval
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Video anomaly detection has been widely applied in various surveillance systems for public security. However, the existing weakly supervised video anomaly detection methods tend to ignore the interference of the background frames and possess limited ability to extract effective temporal information among the video snippets. In this paper, a multi-scale background suppression based anomaly detection (MS-BSAD) method is proposed to suppress the interference of the background frames. We propose a multi-scale temporal convolution module to effectively extracts more temporal information among the video snippets for the anomaly events with different durations. A modified hinge loss is constructed in the suppression branch to help our model to better differentiate the abnormal samples from the confusing samples. Experiments on UCF Crime demonstrate the superiority of our MS-BSAD method in the video anomaly detection task.