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Conference Paper: Robust appearance-based tracking using a sparse Bayesian classifier

TitleRobust appearance-based tracking using a sparse Bayesian classifier
Authors
Issue Date2006
Citation
Proceedings - International Conference On Pattern Recognition, 2006, v. 3, p. 47-50 How to Cite?
AbstractAn appearance-based approach to track an object that may undergo appearance change is proposed. Unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. Through the use of a sparse Bayesian classifier, high classification and detection accuracy can be maintained even if a reduced feature vector is used. In addition, the classifier allows online-training which enables online-updating of the original classification model and provides better adaptability. Experiments show that the method can be used to track targets undergo appearance change due to the change in view-point, facial expression and lighting direction. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/151893
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWong, SFen_US
dc.contributor.authorWong, KYKen_US
dc.contributor.authorCipolla, Ren_US
dc.date.accessioned2012-06-26T06:30:30Z-
dc.date.available2012-06-26T06:30:30Z-
dc.date.issued2006en_US
dc.identifier.citationProceedings - International Conference On Pattern Recognition, 2006, v. 3, p. 47-50en_US
dc.identifier.issn1051-4651en_US
dc.identifier.urihttp://hdl.handle.net/10722/151893-
dc.description.abstractAn appearance-based approach to track an object that may undergo appearance change is proposed. Unlike recent methods that store a detailed representation of object's appearance, this method allows an appearance feature with a reduced dimension to be used. Through the use of a sparse Bayesian classifier, high classification and detection accuracy can be maintained even if a reduced feature vector is used. In addition, the classifier allows online-training which enables online-updating of the original classification model and provides better adaptability. Experiments show that the method can be used to track targets undergo appearance change due to the change in view-point, facial expression and lighting direction. © 2006 IEEE.en_US
dc.languageengen_US
dc.relation.ispartofProceedings - International Conference on Pattern Recognitionen_US
dc.titleRobust appearance-based tracking using a sparse Bayesian classifieren_US
dc.typeConference_Paperen_US
dc.identifier.emailWong, KYK:kykwong@cs.hku.hken_US
dc.identifier.authorityWong, KYK=rp01393en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ICPR.2006.1001en_US
dc.identifier.scopuseid_2-s2.0-34147145931en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34147145931&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3en_US
dc.identifier.spage47en_US
dc.identifier.epage50en_US
dc.publisher.placeUnited Statesen_US
dc.identifier.scopusauthoridWong, SF=7404590193en_US
dc.identifier.scopusauthoridWong, KYK=24402187900en_US
dc.identifier.scopusauthoridCipolla, R=7006935878en_US

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