A new method for violence detection in surveillance scenes

Zhang, T; Yang, Z; Jia, W; Yang, B; Yang, J; He, X

A new method for violence detection in surveillance scenes

Zhang, T Yang, Z Jia, W

Yang, B Yang, J He, X

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Publication Type:: Journal Article
Citation:: Multimedia Tools and Applications, 2016, 75 (12), pp. 7327 - 7349
Issue Date:: 2016-06-01

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Field	Value	Language
dc.contributor.author	Zhang, T	en_US
dc.contributor.author	Yang, Z	en_US
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338	en_US
dc.contributor.author	Yang, B	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2016-06-01	en_US
dc.identifier.citation	Multimedia Tools and Applications, 2016, 75 (12), pp. 7327 - 7349	en_US
dc.identifier.issn	1380-7501	en_US
dc.identifier.uri	http://hdl.handle.net/10453/37590
dc.description.abstract	© 2015, Springer Science+Business Media New York. 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.	en_US
dc.relation.ispartof	Multimedia Tools and Applications	en_US
dc.relation.isbasedon	10.1007/s11042-015-2648-8	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.classification	Software Engineering	en_US
dc.title	A new method for violence detection in surveillance scenes	en_US
dc.type	Journal Article
utslib.citation.volume	12	en_US
utslib.citation.volume	75	en_US
utslib.for	0803 Computer Software	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.issue	12	en_US
pubs.publication-status	Published	en_US
pubs.volume	75	en_US

Abstract:

© 2015, Springer Science+Business Media New York. 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.

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http://hdl.handle.net/10453/37590