Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/77412
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dc.contributor.authorLi, X.en
dc.contributor.authorShen, C.en
dc.contributor.authorDick, A.en
dc.contributor.authorVan Den Hengel, A.en
dc.date.issued2013en
dc.identifier.citationProceedings, 2013 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2013, 23-28 June 2013, Portland, Oregon, USA: pp. 2419-2426en
dc.identifier.isbn9780769549897en
dc.identifier.issn1063-6919en
dc.identifier.urihttp://hdl.handle.net/2440/77412-
dc.description.abstractA key problem in visual tracking is to represent the appearance of an object in a way that is robust to visual changes. To attain this robustness, increasingly complex models are used to capture appearance variations. However, such models can be difficult to maintain accurately and efficiently. In this paper, we propose a visual tracker in which objects are represented by compact and discriminative binary codes. This representation can be processed very efficiently, and is capable of effectively fusing information from multiple cues. An incremental discriminative learner is then used to construct an appearance model that optimally separates the object from its surrounds. Furthermore, we design a hypergraph propagation method to capture the contextual information on samples, which further improves the tracking accuracy. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracker.en
dc.description.statementofresponsibilityXi Li, Chunhua Shen, Anthony Dick, Anton van den Hengelen
dc.description.urihttp://www.pamitc.org/cvpr13/en
dc.language.isoenen
dc.publisherIEEEen
dc.relation.ispartofseriesIEEE Conference on Computer Vision and Pattern Recognitionen
dc.rights©IEEEen
dc.subjectCompact Binary Codes; Random Forest; Visual Trackingen
dc.titleLearning compact binary codes for visual trackingen
dc.typeConference paperen
dc.identifier.rmid0020132963en
dc.contributor.conferenceIEEE Conference on Computer Vision and Pattern Recognition (26th : 2013 : Portland, Oregon)en
dc.identifier.doi10.1109/CVPR.2013.313en
dc.publisher.placeUnited Statesen
dc.identifier.pubid17364-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
Appears in Collections:Computer Science publications

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