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Abnormal event detection via the analysis of multi-frame optical flow information

Abstract : Security surveillance of public scene is closely relevant to routine safety of individual. Under the stimulus of this concern, abnormal event detection is becoming one of the most important tasks in computer vision and video processing. In this paper, we propose a new algorithm to address the visual abnormal detection problem. Our algorithm decouples the problem into a feature descriptor extraction process, followed by an AutoEncoder based network called cascade deep AutoEncoder (CDA). The movement information is represented by a novel descriptor capturing the multi-frame optical flow information. And then, the feature descriptor of the normal samples is fed into the CDA network for training. Finally, the abnormal samples are distinguished by the reconstruction error of the CDA in the testing procedure. We validate the proposed method on several video surveillance datasets.
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https://hal-utt.archives-ouvertes.fr/hal-03320617
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:07:39 AM
Last modification on : Wednesday, October 27, 2021 - 4:29:36 AM

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Tian Wang, Meina Qiao, Aichun Zhu, Guangcun Shan, Hichem Snoussi. Abnormal event detection via the analysis of multi-frame optical flow information. Frontiers of Computer Science, Springer Verlag, 2020, 14 (2), pp.304-313. ⟨10.1007/s11704-018-7407-3⟩. ⟨hal-03320617⟩

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