Biologically inspired features for scene classification in video surveillance

Huang, K; Tao, D; Yuan, Y; Li, X; Tan, T

Biologically inspired features for scene classification in video surveillance

Huang, K Tao, D

Yuan, Y Li, X Tan, T

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41 (1), pp. 307 - 313
Issue Date:: 2011-02-01

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Field	Value	Language
dc.contributor.author	Huang, K	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Tan, T	en_US
dc.date.issued	2011-02-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2011, 41 (1), pp. 307 - 313	en_US
dc.identifier.issn	1083-4419	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15082
dc.description.abstract	Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper. © 2010 IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en_US
dc.relation.isbasedon	10.1109/TSMCB.2009.2037923	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Population Surveillance	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Visual Perception	en_US
dc.subject.mesh	Models, Biological	en_US
dc.subject.mesh	Artificial Intelligence	en_US
dc.subject.mesh	Image Processing, Computer-Assisted	en_US
dc.subject.mesh	Video Recording	en_US
dc.subject.mesh	Databases, Factual	en_US
dc.subject.mesh	Pattern Recognition, Automated	en_US
dc.title	Biologically inspired features for scene classification in video surveillance	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	41	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
dc.location.activity	Zhengzhou, PEOPLES R CHINA
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
utslib.copyright.status	closed_access
pubs.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	41	en_US

Abstract:

Inspired by human visual cognition mechanism, this paper first presents a scene classification method based on an improved standard model feature. Compared with state-of-the-art efforts in scene classification, the newly proposed method is more robust, more selective, and of lower complexity. These advantages are demonstrated by two sets of experiments on both our own database and standard public ones. Furthermore, occlusion and disorder problems in scene classification in video surveillance are also first studied in this paper. © 2010 IEEE.

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http://hdl.handle.net/10453/15082