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dc.contributor.authorQuang, Anh Nguyen
dc.contributor.authorRobles-Kelly, Antonio
dc.contributor.authorZhou, Jun
dc.contributor.editorZha, HB
dc.contributor.editorTaniguchi, RI
dc.contributor.editorMaybank, S
dc.date.accessioned2017-05-03T16:11:48Z
dc.date.available2017-05-03T16:11:48Z
dc.date.issued2010
dc.date.modified2013-06-20T03:46:16Z
dc.identifier.isbn978-3-642-12303-0
dc.identifier.issn0302-9743
dc.identifier.doi10.1007/978-3-642-12304-7_22
dc.identifier.urihttp://hdl.handle.net/10072/51670
dc.description.abstractIn this paper, we present a feature combination approach to object tracking based upon graph embedding techniques. The method presented here abstracts the low complexity features used for purposes of tracking to a relational structure and employs graph-spectral methods to combine them. This gives rise to a feature combination scheme which minimises the mutual cross-correlation between features and is devoid of free parameters. It also allows an analytical solution making use of matrix factorisation techniques. The new target location is recovered making use of a weighted combination of target-centre shifts corresponding to each of the features under study, where the feature weights arise from a cost function governed by the embedding process. This treatment permits the update of the feature weights in an on-line fashion in a straightforward manner. We illustrate the performance of our method in real-world image sequences and compare our results to a number of alternatives.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent809877 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherSpringer
dc.publisher.placeGermany
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencename9th Asian Conference on Computer Vision
dc.relation.ispartofconferencetitleCOMPUTER VISION - ACCV 2009, PT II
dc.relation.ispartofdatefrom2009-09-23
dc.relation.ispartofdateto2009-09-27
dc.relation.ispartoflocationXian, PEOPLES R CHINA
dc.relation.ispartofpagefrom224
dc.relation.ispartofpageto235
dc.relation.ispartofissuePART 2
dc.relation.ispartofvolume5995
dc.rights.retentionY
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPattern Recognition and Data Mining
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode080109
dc.titleA Graph-based Feature Combination Approach to Object Tracking
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2009 Springer Berlin/Heidelberg. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
gro.date.issued2010
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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