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Fast Abnormal Event Detection

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Abstract

Fast abnormal event detection meets the growing demand to process an enormous number of surveillance videos. Based on the inherent redundancy of video structures, we propose an efficient sparse combination learning framework with both batch and online solvers. It achieves decent performance in the detection phase without compromising result quality. The extremely fast execution speed is guaranteed owing to the fact that our method effectively turns the original complicated problem into a few small-scale least square optimizations. Our method reaches high detection rates on benchmark datasets at a speed of 1000–1200 frames per second on average when computing on an ordinary single core desktop PC using MATLAB.

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Acknowledgements

This work is supported by the National Science Foundation China, under Grant 61772332,51675342,61133009 and by a Grant from the Research Grants Council of the Hong Kong SAR (Project No. 2150760).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Cewu Lu

  2. Sensetime Group Limited, Beijing, China

    Jianping Shi

  3. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Weiming Wang

  4. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China

    Jiaya Jia

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  1. Cewu Lu
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  2. Jianping Shi
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  3. Weiming Wang
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  4. Jiaya Jia
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Correspondence to Cewu Lu or Weiming Wang.

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Communicated by Kristen Grauman.

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Lu, C., Shi, J., Wang, W. et al. Fast Abnormal Event Detection. Int J Comput Vis 127, 993–1011 (2019). https://doi.org/10.1007/s11263-018-1129-8

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  • DOI: https://doi.org/10.1007/s11263-018-1129-8

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