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Journal Articles AIP Advances Year : 2018

Abnormal global and local event detection in compressive sensing domain

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Tian Wang
  • Function : Author
Meina Qiao
  • Function : Author
Jie Chen
Wenjia Zhang
  • Function : Author

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.

Dates and versions

hal-03320618 , version 1 (16-08-2021)

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Cite

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