? 基于协同表达的异常事件检测
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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 << Previous Articles | Next Articles >>
基于协同表达的异常事件检测
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
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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摘要 拥挤场景下的异常事件检测是计算机视觉和信息检索领域的热点问题。本文中,我们研究视频中异常行为的检测问题,并提出一种鲁棒的多特征描述协同表达算法进行异常事件检测。该算法在同一个表达中描述不同的特征,即认为相同主题的不同特征表现出更多相同的特性。然后,我们建立不同特征描述间的本证关系,并获取视频序列的变化信息,这可以鲁棒地区分异常和正常事件。该算法在两个基准数据集上进行了实验验证,与目前最后的一些算法对比结果证明了我们算法的有效性。
关键词异常检测   协同表达   字典学习     
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.
Keywordsabnormal detection   collective representation   dictionary learning     
Received 2016-12-26;
本文基金:

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.

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.
引用本文:   
Renzhen Ye, Xuelong Li.基于协同表达的异常事件检测[J]  Journal of Computer Science and Technology , 2017,V32(3): 470-479
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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