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Local abnormal behavior detection based on optical flow and spatio-temporal gradient

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Abstract

To improve the accuracy of the detection of local abnormal behavior, a novel method is here proposed. The main idea of the proposed method is described as follows: firstly, a video sequence is divided into spatio-temporal blobs; then, a statistical method based on the semi-parametric model is adopted to detect these blobs where abnormal behaviors most likely to appear; finally, maximum optical flow energy and local nearest descriptor are utilized to determinate whether these suspicious blobs really contain abnormal behaviors. The experimental results conducted on several benchmarks ademonstrate the effectiveness of the proposed method.

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Acknowledgments

This work is supported by Postdoctoral Foundation of China under No. 2014 M550297, Postdoctoral Foundation of Jiangsu Province under No. 1302087B, Graduate Education Reform Research and Practice Program of Jiangsu Province under No. JGZZ13_041 and JGLX15_055, Graduate Research and Innovation Program of Jiangsu under No. KYLX15_0854 and No. SJZZ15_0105.

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Authors and Affiliations

  1. School of Automatic, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China

    Songhao Zhu, Juanjuan Hu & Zhe Shi

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  1. Songhao Zhu
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  2. Juanjuan Hu
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  3. Zhe Shi
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Correspondence to Songhao Zhu.

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Zhu, S., Hu, J. & Shi, Z. Local abnormal behavior detection based on optical flow and spatio-temporal gradient. Multimed Tools Appl 75, 9445–9459 (2016). https://doi.org/10.1007/s11042-015-3122-3

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  • DOI: https://doi.org/10.1007/s11042-015-3122-3

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