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dc.contributor.authorTopçu, O.
dc.contributor.authorOrguner, U.
dc.contributor.authorAlatan, A. A.
dc.contributor.authorErcan, Ali Özer
dc.date.accessioned2016-02-16T10:26:02Z
dc.date.available2016-02-16T10:26:02Z
dc.date.issued2014
dc.identifier.isbn978-1-4799-4874-1
dc.identifier.issn2165-0608
dc.identifier.urihttp://hdl.handle.net/10679/2551
dc.identifier.urihttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6830318
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractVisual tracking has an important place among computer vision applications. Visual tracking with particle filters is a well-known methodology. The performance of particle filters is dependent on efficient sampling of the state space, which in turn, is dependent on number of particles. In this paper, Rao-Blackwell technique is applied to particle filters to improve sampling efficiency. Both algorithms are applied to people tracking problem. Under the same circumstances, the resulting algorithm is demonstrated to perform better than the original algorithm via experiments on the PETS2009 benchmark dataset.
dc.language.isoturen_US
dc.publisherIEEE
dc.relation.ispartofSignal Processing and Communications Applications Conference (SIU), 2014 22nd
dc.rightsrestrictedAccess
dc.titleRao-Blackwell parçacık süzgeci ile 3 boyutlu insan takibien_US
dc.title.alternative3D tracking of people with rao-blackwellized particle filters
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0003-1126-8259 & YÖK ID 35788) Ercan, Ali
dc.contributor.ozuauthorErcan, Ali Özer
dc.identifier.startpage670
dc.identifier.endpage673
dc.identifier.wosWOS:000356351400147
dc.identifier.doi10.1109/SIU.2014.6830318
dc.subject.keywordsVisual tracking
dc.subject.keywordsRao-Blackwellization
dc.subject.keywordsMarginalization
dc.subject.keywordsOcclusion
dc.subject.keywordsParticle filter
dc.subject.keywordsMulti-camera
dc.identifier.scopusSCOPUS:2-s2.0-84903762691
dc.contributor.authorMale1
dc.relation.publicationcategoryConference Paper - International - Institutional Academic Staff


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