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International Conference in Communications, Signal Processing, and Systems

CSPS 2018: Communications, Signal Processing, and Systems pp 1217–1225Cite as

Abnormal Event Detection and Localization in Visual Surveillance

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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 516))

Abstract

In this paper, we propose a framework for abnormal event detection and analysis in the field of visual surveillance based on the state-of-the-art deep learning techniques. We train a pair of conditional generative adversarial networks (cGANs) using the normal behavior samples, where one cGAN takes video frames as inputs and generates the corresponding optical flow features. While on the other hand, the other cGANs take optical flow features as inputs and generate the corresponding video frames. By analyzing the differences between the generated frames/optical flow features and the realistic samples, abnormal events can be detected and localized effectively. Moreover, for suspected regions, we adopt the faster RCNN to analyze the abnormal events. Experimental results demonstrate that the proposed framework can detect the abnormal events accurately and efficiently.

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Acknowledgements

This work is partly supported by the National Natural Science Foundation of China (Grant No. 61702073) and the Fundamental Research Funds for the Central Universities (Grant No. 3132018190).

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Authors and Affiliations

  1. College of Information Science and Technology, Dalian Maritime University, Linghai Road 1, Dalian, 116026, China

    Yonglin Mu & Bo Zhang

Authors
  1. Yonglin Mu
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  2. Bo Zhang
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Corresponding author

Correspondence to Bo Zhang .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, TX, USA

    Qilian Liang

  2. School of Information and Communication Engineering, Dalian University of Technology, Dalian, China

    Xin Liu

  3. School of Information Science and Technology, Dalian Maritime University, Dalian, China

    Zhenyu Na

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Cite this paper

Mu, Y., Zhang, B. (2020). Abnormal Event Detection and Localization in Visual Surveillance. In: Liang, Q., Liu, X., Na, Z., Wang, W., Mu, J., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2018. Lecture Notes in Electrical Engineering, vol 516. Springer, Singapore. https://doi.org/10.1007/978-981-13-6504-1_145

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  • DOI: https://doi.org/10.1007/978-981-13-6504-1_145

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6503-4

  • Online ISBN: 978-981-13-6504-1

  • eBook Packages: EngineeringEngineering (R0)

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