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Anomaly detection in crowded scenes using motion energy model

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Abstract

We present a new method for detection of abnormal behaviors in crowded scenes. Based on statistics of low-level feature—optical flow, which describes human movement efficiently, the motion energy model is proposed to represent the local motion pattern in the crowd. The model stresses the difference between normal and abnormal behaviors by considering sum of square differences (SSD) metric of motion information in the center block and its neighboring blocks. Meanwhile, data increasing rate is introduced to filter outliers to achieve boundary values between abnormal and normal motion patterns. In this model, an abnormal behavior is detected if the occurrence probability of anomaly is higher than a preset threshold, namely the motion energy value of its corresponding block is higher than that of the normal one. We evaluate the proposed method on two public available datasets, showing competitive performance with respect to state-of-the-art approaches not only in detection accuracy, but also in computational efficiency.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant 61471262, the International (Regional) Cooperation and Exchange under Grant 61520106002, and the Doctoral Fund of Ministry of Education of China under Grant 20130032110010.

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Authors and Affiliations

  1. School of Electronics and Information Engineering, Tianjin University, Tianjin, 300072, China

    Tianyu Chen, Chunping Hou & Zhipeng Wang

  2. Faculty of Information Science and Engineering, Ningbo University, Ningbo, China

    Hua Chen

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  1. Tianyu Chen
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  2. Chunping Hou
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  3. Zhipeng Wang
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  4. Hua Chen
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Correspondence to Tianyu Chen.

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Chen, T., Hou, C., Wang, Z. et al. Anomaly detection in crowded scenes using motion energy model. Multimed Tools Appl 77, 14137–14152 (2018). https://doi.org/10.1007/s11042-017-5020-3

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  • DOI: https://doi.org/10.1007/s11042-017-5020-3

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