Multimedia Tools and Applications

, Volume 75, Issue 12, pp 7327–7349 | Cite as

A new method for violence detection in surveillance scenes

Article

Abstract

Violence detection is a hot topic for surveillance systems. However, it has not been studied as much as for action recognition. Existing vision-based methods mainly concentrate on violence detection and make little effort to determine the location of violence. In this paper, we propose a fast and robust framework for detecting and localizing violence in surveillance scenes. For this purpose, a Gaussian Model of Optical Flow (GMOF) is proposed to extract candidate violence regions, which are adaptively modeled as a deviation from the normal behavior of crowd observed in the scene. Violence detection is then performed on each video volume constructed by densely sampling the candidate violence regions. To distinguish violent events from nonviolent events, we also propose a novel descriptor, named as Orientation Histogram of Optical Flow (OHOF), which are fed into a linear SVM for classification. Experimental results on several benchmark datasets have demonstrated the superiority of our proposed method over the state-of-the-arts in terms of both detection accuracy and processing speed, even in crowded scenes.

Keywords

Action recognition Violence detection Surveillance scenes Gaussian model of optical flow (GMOF) Orientation histogram of optical flow (OHOF) 

Notes

Acknowledgments

This research is partly supported by NSFC, China (No: 61273258, 61105001).

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Institute of Image Processing and Pattern RecognitionShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Faculty of Engineering and Information TechnologyUniversity of Technology, SydneySydneyAustralia

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