On combining compressed sensing and sparse representations for object tracking

Sun, H; Li, J; Du, B; Tao, D

On combining compressed sensing and sparse representations for object tracking

Sun, H Li, J Du, B Tao, D

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9916 LNCS pp. 32 - 43
Issue Date:: 2016-01-01

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Field	Value	Language
dc.contributor.author	Sun, H	en_US
dc.contributor.author	Li, J	en_US
dc.contributor.author	Du, B	en_US
dc.contributor.author	Tao, D https://orcid.org/0000-0001-7225-5449	en_US
dc.date.issued	2016-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9916 LNCS pp. 32 - 43	en_US
dc.identifier.isbn	9783319488899	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/105826
dc.description.abstract	© Springer International Publishing AG 2016. The tracking algorithm of compressed sensing takes advantage of the objective’s background information, but lacks the feedback mechanism towards the results. The 11 sparse tracking algorithm adapts to the changes in the objectives’ appearances but at the cost of losing their background information. To enhance the effectiveness and robustness of the algorithm in coping with such distractions as occlusion and illumination variation, this paper proposes a tracking framework with the 11 sparse representation being the detector and compressed sensing algorithm the tracker, and establishes a complementary classifier model. A second-order model updating strategy has therefore been proposed to preserve the most representative templates in the 11 sparse representations. It is concluded that this tracking algorithm is better than the prevalent 8 ones with a respective precision plot of 77.15%, 72.33% and 81.13% and a respective success plot of 77.67%, 74.01%, 81.51% in terms of the overall, occlusion and illumination variation.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-48890-5_4	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	On combining compressed sensing and sparse representations for object tracking	en_US
dc.type	Conference Proceeding
utslib.citation.volume	9916 LNCS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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.publication-status	Published	en_US
pubs.volume	9916 LNCS	en_US

Abstract:

© Springer International Publishing AG 2016. The tracking algorithm of compressed sensing takes advantage of the objective’s background information, but lacks the feedback mechanism towards the results. The 11 sparse tracking algorithm adapts to the changes in the objectives’ appearances but at the cost of losing their background information. To enhance the effectiveness and robustness of the algorithm in coping with such distractions as occlusion and illumination variation, this paper proposes a tracking framework with the 11 sparse representation being the detector and compressed sensing algorithm the tracker, and establishes a complementary classifier model. A second-order model updating strategy has therefore been proposed to preserve the most representative templates in the 11 sparse representations. It is concluded that this tracking algorithm is better than the prevalent 8 ones with a respective precision plot of 77.15%, 72.33% and 81.13% and a respective success plot of 77.67%, 74.01%, 81.51% in terms of the overall, occlusion and illumination variation.

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http://hdl.handle.net/10453/105826