Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/61894
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dc.contributor.authorShen, C.en
dc.contributor.authorKim, J.en
dc.contributor.authorWang, H.en
dc.date.issued2010en
dc.identifier.citationIEEE Transactions on Circuits and Systems for Video Technology, 2010; 20(1):119-130en
dc.identifier.issn1051-8215en
dc.identifier.issn1558-2205en
dc.identifier.urihttp://hdl.handle.net/2440/61894-
dc.description.abstractKernel-based mean shift (MS) trackers have proven to be a promising alternative to stochastic particle filtering trackers. Despite its popularity, MS trackers have two fundamental drawbacks: 1) the template model can only be built from a single image, and 2) it is difficult to adaptively update the template model. In this paper, we generalize the plain MS trackers and attempt to overcome these two limitations. It is well known that modeling and maintaining a representation of a target object is an important component of a successful visual tracker. However, little work has been done on building a robust template model for kernel-based MS tracking. In contrast to building a template from a single frame, we train a robust object representation model from a large amount of data. Tracking is viewed as a binary classification problem, and a discriminative classification rule is learned to distinguish between the object and background. We adopt a support vector machine for training. The tracker is then implemented by maximizing the classification score. An iterative optimization scheme very similar to MS is derived for this purpose. Compared with the plain MS tracker, it is now much easier to incorporate online template adaptation to cope with inherent changes during the course of tracking. To this end, a sophisticated online support vector machine is used. We demonstrate successful localization and tracking on various data sets.en
dc.description.statementofresponsibilityChunhua Shen, Junae Kim, and Hanzi Wangen
dc.language.isoenen
dc.publisherIEEE-Inst Electrical Electronics Engineers Incen
dc.rightsCopyright 2010 IEEEen
dc.subjectGlobal mode seeking; kernel-based tracking; mean shift; particle filter; support vector machineen
dc.titleGeneralized kernel-based visual trackingen
dc.typeJournal articleen
dc.identifier.rmid0020100129en
dc.identifier.doi10.1109/TCSVT.2009.2031393en
dc.identifier.pubid33784-
pubs.library.collectionComputer Science publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidShen, C. [0000-0002-8648-8718]en
Appears in Collections:Computer Science publications

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