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Abnormal Events Detection Using Deep Networks for Video Surveillance

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Cognitive Systems and Signal Processing (ICCSIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 710))

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Abstract

In this paper, a novel method is proposed to detect abnormal events. This method is based on spatio-temporal deep networks which can represent sequential video frames. Abnormal events are rare in real world and involve small samples along with large amount of normal video data. It is difficult to apply with deep networks directly which usually require amounts of labeled samples. Our method solves this problem by pre-training the networks on videos which are irrelevant to abnormal events and refining the networks with fine tuning. Furthermore, we employ the patch strategy to improve the performance of our method in complex scenes. The proposed method is tested on real surveillance videos which only contain limited abnormal samples. Experimental results show that the proposed approach can outperform the conventional abnormal event detection algorithm which utilized hand-crafted features.

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Authors and Affiliations

  1. School of Automation Engineering, Center for Robotics, University of Electronic Science and Technology of China, Chengdu, China

    Binghao Meng, Lu Zhang, Fan Jin, Lu Yang & Hong Cheng

  2. Ricoh Software Research Center of Beijing, Beijing, China

    Qian Wang

Authors
  1. Binghao Meng
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  2. Lu Zhang
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  3. Fan Jin
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  4. Lu Yang
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  5. Hong Cheng
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  6. Qian Wang
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Corresponding author

Correspondence to Hong Cheng .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Huaping Liu

  3. College of Mechatronics and Automation, National University of Defense Technology, Changsha, China

    Dewen Hu

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Meng, B., Zhang, L., Jin, F., Yang, L., Cheng, H., Wang, Q. (2017). Abnormal Events Detection Using Deep Networks for Video Surveillance. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_22

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  • DOI: https://doi.org/10.1007/978-981-10-5230-9_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5229-3

  • Online ISBN: 978-981-10-5230-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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