Incremental learning of weighted tensor subspace for visual tracking

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

Incremental learning of weighted tensor subspace for visual tracking

Wen, J Li, X Gao, X Tao, D

Permalink

Publication Type:: Conference Proceeding
Citation:: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 3688 - 3693
Issue Date:: 2009-12-01

Closed Access

	Filename	Description	Size
	2011001264OK.pdf		472.35 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Wen, J	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Gao, X	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, 2009, pp. 3688 - 3693	en_US
dc.identifier.isbn	9781424427949	en_US
dc.identifier.issn	1062-922X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/17295
dc.description.abstract	Tensor analysis has been widely utilized in imagerelated machine learning applications, which has preferable performance over the vector-based approaches for its capability of holding the spatial structure information in some research field. The traditional tensor representation only includes the intensity values, which is sensitive to illumination variation. For this purpose, a weighted tensor subspace (WTS) is defined as object descriptor by combining the Retinex image with the original image. Then, an incremental learning algorithm is developed for WTS to adapt to the appearance change during the tracking. The proposed method could learn the lightness changing incrementally and get robust tracking performance under various luminance conditions. The experimental results illustrate the effectiveness of the proposed visual tracking Scheme. ©2009 IEEE.	en_US
dc.relation.ispartof	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICSMC.2009.5346874	en_US
dc.title	Incremental learning of weighted tensor subspace for visual tracking	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	San Antonio, TX	en_US
dc.location.activity	Helsinki, Finland
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.publication-status	Published	en_US

Abstract:

Tensor analysis has been widely utilized in imagerelated machine learning applications, which has preferable performance over the vector-based approaches for its capability of holding the spatial structure information in some research field. The traditional tensor representation only includes the intensity values, which is sensitive to illumination variation. For this purpose, a weighted tensor subspace (WTS) is defined as object descriptor by combining the Retinex image with the original image. Then, an incremental learning algorithm is developed for WTS to adapt to the appearance change during the tracking. The proposed method could learn the lightness changing incrementally and get robust tracking performance under various luminance conditions. The experimental results illustrate the effectiveness of the proposed visual tracking Scheme. ©2009 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/17295