View-Independent Behavior Analysis

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

View-Independent Behavior Analysis

Huang, K Tan, T Tao, D

Yuan, Y Li, X

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39 (4), pp. 1028 - 1035
Issue Date:: 2009-01-01

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Field	Value	Language
dc.contributor.author	Huang, K	en_US
dc.contributor.author	Tan, T	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.date.issued	2009-01-01	en_US
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2009, 39 (4), pp. 1028 - 1035	en_US
dc.identifier.issn	1083-4419	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15104
dc.description.abstract	The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance. Most research in behavior (or gesture) representation focusses on view-dependent representation, and some research on view invariance considers only information from 3-D models, which is effective under considerable changes of viewpoint. This paper introduces a view-independent behavior-analysis framework based on decision fusion in which distance and view angle factors are analyzed. This is a first effort to tackle the problem of behaviors under significant changes in view angle, and a first corresponding video database is built. © 2009, IEEE.	en_US
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics	en_US
dc.relation.isbasedon	10.1109/TSMCB.2008.2011815	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Bayes Theorem	en_US
dc.subject.mesh	Behavior	en_US
dc.subject.mesh	Movement	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Models, Theoretical	en_US
dc.subject.mesh	Human Body	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.title	View-Independent Behavior Analysis	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	39	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
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	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

The motion analysis of the human body is an important topic of research in computer vision devoted to detecting, tracking, and understanding people's physical behavior. This strong interest is driven by a wide spectrum of applications in various areas such as smart video surveillance. Most research in behavior (or gesture) representation focusses on view-dependent representation, and some research on view invariance considers only information from 3-D models, which is effective under considerable changes of viewpoint. This paper introduces a view-independent behavior-analysis framework based on decision fusion in which distance and view angle factors are analyzed. This is a first effort to tackle the problem of behaviors under significant changes in view angle, and a first corresponding video database is built. © 2009, IEEE.

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http://hdl.handle.net/10453/15104