Incremental tensor biased discriminant analysis: A new color-based visual tracking method

Wen, J; Gao, X; Yuan, Y; Tao, D; Li, J

Incremental tensor biased discriminant analysis: A new color-based visual tracking method

Wen, J Gao, X Yuan, Y Tao, D

Li, J

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Publication Type:: Journal Article
Citation:: Neurocomputing, 2010, 73 (4-6), pp. 827 - 839
Issue Date:: 2010-01-01

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Field	Value	Language
dc.contributor.author	Wen, J	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Yuan, Y	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.contributor.author	Li, J	en_US
dc.date.issued	2010-01-01	en_US
dc.identifier.citation	Neurocomputing, 2010, 73 (4-6), pp. 827 - 839	en_US
dc.identifier.issn	0925-2312	en_US
dc.identifier.uri	http://hdl.handle.net/10453/15172
dc.description.abstract	Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2009.10.013	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Incremental tensor biased discriminant analysis: A new color-based visual tracking method	en_US
dc.type	Journal Article
utslib.citation.volume	4-6	en_US
utslib.citation.volume	73	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	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	4-6	en_US
pubs.publication-status	Published	en_US
pubs.volume	73	en_US

Abstract:

Most existing color-based tracking algorithms utilize the statistical color information of the object as the tracking clues, without maintaining the spatial structure within a single chromatic image. Recently, the researches on the multilinear algebra provide the possibility to hold the spatial structural relationship in a representation of the image ensembles. In this paper, a third-order color tensor is constructed to represent the object to be tracked. Considering the influence of the environment changing on the tracking, the biased discriminant analysis (BDA) is extended to the tensor biased discriminant analysis (TBDA) for distinguishing the object from the background. At the same time, an incremental scheme for the TBDA is developed for the tensor biased discriminant subspace online learning, which can be used to adapt to the appearance variant of both the object and background. The experimental results show that the proposed method can track objects precisely undergoing large pose, scale and lighting changes, as well as partial occlusion. © 2009 Elsevier B.V.

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http://hdl.handle.net/10453/15172