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Collective Representation for Abnormal Event Detection

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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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Authors and Affiliations

  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

    Renzhen Ye & Xuelong Li

  2. School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710119, China

    Renzhen Ye

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  1. Renzhen Ye
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  2. Xuelong Li
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Correspondence to Renzhen Ye.

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Ye, R., Li, X. Collective Representation for Abnormal Event Detection. J. Comput. Sci. Technol. 32, 470–479 (2017). https://doi.org/10.1007/s11390-017-1737-8

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  • DOI: https://doi.org/10.1007/s11390-017-1737-8

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