Visual tracking based on weighted subspace reconstruction error

Zhou, T; Zhang, J; Xie, K; Yang, J; He, X

Visual tracking based on weighted subspace reconstruction error

Zhou, T Zhang, J Xie, K Yang, J He, X

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Publication Type:: Conference Proceeding
Citation:: 2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, pp. 461 - 465
Issue Date:: 2014-01-28

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Field	Value	Language
dc.contributor.author	Zhou, T	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Xie, K	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	He, X https://orcid.org/0000-0001-8962-540X	en_US
dc.date.issued	2014-01-28	en_US
dc.identifier.citation	2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, pp. 461 - 465	en_US
dc.identifier.isbn	9781479957514	en_US
dc.identifier.uri	http://hdl.handle.net/10453/34106
dc.description.abstract	© 2014 IEEE. It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven state-of-the-art methods in terms of accuracy and robustness.	en_US
dc.relation.ispartof	2014 IEEE International Conference on Image Processing, ICIP 2014	en_US
dc.relation.isbasedon	10.1109/ICIP.2014.7025092	en_US
dc.title	Visual tracking based on weighted subspace reconstruction error	en_US
dc.type	Conference Proceeding
utslib.for	080104 Computer Vision	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	080106 Image Processing	en_US
dc.location.activity	Paris
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2014 IEEE. It is a challenging task to develop an effective and robust visual tracking method due to factors such as pose variation, illumination change, occlusion, and motion blur. In this paper, a novel tracking algorithm based on weighted subspace reconstruction error is proposed. We first compute the discriminative weights by sparse construction error with template dictionary consisted of positive and negative samples, and then confidence map for candidates is computed through subspace reconstruction error. Finally, the location of the target object is estimated by maximizing the decision map which is combined discriminative weights and subspace reconstruction error. Furthermore, we use the new evaluation criterion to verify the robustness of the current tracking result, which can reduce the accumulated error effectively. Experimental results on some challenging video sequences show that the proposed algorithm performs favorably against seven state-of-the-art methods in terms of accuracy and robustness.

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http://hdl.handle.net/10453/34106