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Robust and efficient anomaly detection using heterogeneous representations

[+] Author Affiliations
Xing Hu, Shiqiang Hu

Shanghai Jiao Tong University, School of Aeronautics and Astronautics, Dongchuan Road 800, Shanghai 200240, China

Jinhua Xie, Shiyou Zheng

Radar and Avionics Institute of AVIC, Liangxi Road 796, Wuxi, Jiangsu 214063, China

J. Electron. Imaging. 24(3), 033021 (Jun 10, 2015). doi:10.1117/1.JEI.24.3.033021
History: Received December 10, 2014; Accepted May 13, 2015
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Abstract.  Various approaches have been proposed for video anomaly detection. Yet these approaches typically suffer from one or more limitations: they often characterize the pattern using its internal information, but ignore its external relationship which is important for local anomaly detection. Moreover, the high-dimensionality and the lack of robustness of pattern representation may lead to problems, including overfitting, increased computational cost and memory requirements, and high false alarm rate. We propose a video anomaly detection framework which relies on a heterogeneous representation to account for both the pattern’s internal information and external relationship. The internal information is characterized by slow features learned by slow feature analysis from low-level representations, and the external relationship is characterized by the spatial contextual distances. The heterogeneous representation is compact, robust, efficient, and discriminative for anomaly detection. Moreover, both the pattern’s internal information and external relationship can be taken into account in the proposed framework. Extensive experiments demonstrate the robustness and efficiency of our approach by comparison with the state-of-the-art approaches on the widely used benchmark datasets.

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Citation

Xing Hu ; Shiqiang Hu ; Jinhua Xie and Shiyou Zheng
"Robust and efficient anomaly detection using heterogeneous representations", J. Electron. Imaging. 24(3), 033021 (Jun 10, 2015). ; http://dx.doi.org/10.1117/1.JEI.24.3.033021


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