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Abnormal global and local event detection in compressive sensing domain

Abstract : Abnormal event detection, also known as anomaly detection, is one challenging task in security video surveillance. It is important to develop effective and robust movement representation models for global and local abnormal event detection to fight against factors such as occlusion and illumination change. In this paper, a new algorithm is proposed. It can locate the abnormal events on one frame, and detect the global abnormal frame. The proposed algorithm employs a sparse measurement matrix designed to represent the movement feature based on optical flow efficiently. Then, the abnormal detection mission is constructed as a one-class classification task via merely learning from the training normal samples. Experiments demonstrate that our algorithm performs well on the benchmark abnormal detection datasets against state-of-the-art methods.
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https://hal-utt.archives-ouvertes.fr/hal-03320618
Contributor : Jean-Baptiste Vu Van Connect in order to contact the contributor
Submitted on : Monday, August 16, 2021 - 11:09:43 AM
Last modification on : Wednesday, October 13, 2021 - 7:16:03 PM

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Tian Wang, Meina Qiao, Jie Chen, Chuanyun Wang, Wenjia Zhang, et al.. Abnormal global and local event detection in compressive sensing domain. AIP Advances, American Institute of Physics- AIP Publishing LLC, 2018, 8 (5), pp.055224. ⟨10.1063/1.5022779⟩. ⟨hal-03320618⟩

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