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Anomaly detection based on spatio-temporal sparse representation and visual attention analysis

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Abstract

In this paper, we proposed a unified framework for anomaly detection and localization in crowed scenes. For each video frame, we extract the spatio-temporal sparse features of 3D blocks and generate the saliency map using a block-based center-surround difference operator. Two sparse coding strategies including off-line long-term sparse representation and on-line short-term sparse representation are integrated within our framework. Abnormality of each candidate is measured using bottom-up saliency and top-down fixation inference and further used to classify the frames into normal and anomalous ones by a binary classifier. Local abnormal events are localized and segmented based on the saliency map. In the experiments, we compared our method against several state-of-the-art approaches on UCSD data set which is a widely used anomaly detection and localization benchmark. Our method outputs competitive results with near real-time processing speed compared to state-of-the-arts.

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Acknowledgments

The work was supported in part by the Special Social Science Foundation of Heilongjiang Province of China No. 11D083, and the National Science Foundation of China No. 61472103, 2015BAF32B01-4.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, No. 92, West Dazhi Street, Heilongjiang, China, 150001

    Chen Wang, Hongxun Yao & Xiaoshuai Sun

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  1. Chen Wang
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  2. Hongxun Yao
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  3. Xiaoshuai Sun
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Correspondence to Chen Wang.

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Wang, C., Yao, H. & Sun, X. Anomaly detection based on spatio-temporal sparse representation and visual attention analysis. Multimed Tools Appl 76, 6263–6279 (2017). https://doi.org/10.1007/s11042-015-3199-8

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  • DOI: https://doi.org/10.1007/s11042-015-3199-8

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