? Collective Representation for Abnormal Event Detection
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (3) :470-479    DOI: 10.1007/s11390-017-1737-8
Special Issue on Software Engineering for High-Confidence Systems Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
Collective Representation for Abnormal Event Detection
Renzhen Ye1,2, Xuelong Li1, Fellow, IEEE
1. Center for Optical Imagery Analysis and Learning(OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China;
2. School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710119, China

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Abstract Abnormal event detection in crowded scenes is a hot topic in computer vision and information retrieval community. In this paper, we study the problems of detecting anomalous behaviors within the video, and propose a robust collective representation with multi-feature descriptors for abnormal event detection. The proposed method represents different features in an identical representation, in which different features of the same topic will show more common properties. Then, we build the intrinsic relation between different feature descriptors and capture concept drift in the video sequence, which can robustly discriminate between abnormal events and normal events. Experimental results on two benchmark datasets and the comparison with the state-of-the-art methods validate the effectiveness of our method.
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Renzhen Ye
Xuelong Li
Keywordsabnormal detection   collective representation   dictionary learning     
Received 2016-12-26;
Fund:

This work is supported by the Defense Basic Research Program under Grant No. BA1320110042, and the Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences under Grant No. LSIT201408.

Corresponding Authors: 10.1007/s11390-017-1737-8   
About author: Renzhen Ye is currently an associate professor with the Department of Mathematics, Huazhong Agricultural University, Wuhan. She is pursuing her Ph.D. degree in the Center for OPTical IMagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an. Her research interests include partial differential equations, mathematical mechanization and mathematical physics, and machine learning.
Cite this article:   
Renzhen Ye, Xuelong Li.Collective Representation for Abnormal Event Detection[J]  Journal of Computer Science and Technology, 2017,V32(3): 470-479
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http://jcst.ict.ac.cn:8080/jcst/EN/10.1007/s11390-017-1737-8
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