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Abnormal event detection via the analysis of multi-frame optical flow information |
Tian WANG1, Meina QIAO1, Aichun ZHU2, Guangcun SHAN3( ), Hichem SNOUSSI4 |
1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2. School of Computer Science and Technology, Nanjing University of Technology, Nanjing 210094, China
3. School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing 100191, China
4. Institute Charles Delaunay-LM2S-UMR STMR 6281 CNRS, University of Technology of Troyes, Troyes 10010, France |
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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 themulti-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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| Keywords
abnormal event detection
multi-frame optical flow
cascade deep autoencoder
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Corresponding Author(s):
Guangcun SHAN
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Just Accepted Date: 01 August 2018
Online First Date: 17 September 2019
Issue Date: 16 October 2019
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