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Abnormal Event Detection and Localization in Visual Surveillance

  • Yonglin Mu
  • Bo ZhangEmail author
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (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.

Keywords

Conditional GANs Faster RCNN Abnormal event detection Visual surveillance 

Notes

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Information Science and TechnologyDalian Maritime UniversityDalianChina

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