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Abnormal Event Detection Based on Deep Autoencoder Fusing Optical Flow

Qiao, Meina, Wang, Tian, Li, Jiakun, Li, Ce, Lin, Zhiwei and Snoussi, Hichem (2017) Abnormal Event Detection Based on Deep Autoencoder Fusing Optical Flow. In: The Chinese Control Conference 2017, Dalian, China. IEEE. 6 pp. [Conference contribution]

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DOI: 10.23919/ChiCC.2017.8029129

Abstract

As an important research topic in computer vision, abnormal detection has gained more and more attention. In order to detect abnormal events effectively, we propose a novel method using optical flow and deep autoencoder. In our model, optical flow of the original video sequence is calculated and visualized as optical flow image, which is then fed into a deep autoencoder. Then the deep autoencoder extract features from the training samples which are compressed to low dimension vectors. Finally, the normal and abnormal samples gather separately in the coordinate axis. In the evaluation, we show that our approach outperforms the existing methods in different scenes, in terms of accuracy.

Item Type:Conference contribution (Paper)
Keywords:Abnormal detection, Deep autoencoder, Optical flow
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:37614
Deposited By: Dr Zhiwei Lin
Deposited On:26 Apr 2017 13:47
Last Modified:17 Oct 2017 16:29

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