A new method for violence detection in surveillance scenes

Tao Zhang, Zhijie Yang, Wenjing Jia, Baoqing Yang, Jie Yang, Xiangjian He

Research output: Journal PublicationArticlepeer-review

75 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)7327-7349
Number of pages23
JournalMultimedia Tools and Applications
Volume75
Issue number12
DOIs
Publication statusPublished - 1 Jun 2016
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Software
  • Media Technology
  • Hardware and Architecture
  • Computer Networks and Communications

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