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dc.contributor.authorTopçu, O..
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.isbn978-1-4799-4871-0
dc.identifier.urihttp://hdl.handle.net/10679/2550
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6918644
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractAn occlusion-aware multiple deformable object tracker for visual surveillance from two cameras is presented. Each object is tracked by a separate particle filter tracker, which is initiated upon detection of a new person and terminated when s/he leaves the scene. Objects are considered as 3D points at their centre of masses as if their mass density is uniform. Point objects and corresponding silhouette centroids in two views together with the epipolar geometry they satisfy resulted in a practical tracking methodology. An occlusion filter is described, that provides the tracker filters conditional occlusion probabilities of the objects, given their estimated positions. Advances over the previous work; in the computation of conditional occlusion probabilities, in incorporation of these probabilities in the particle filter, and in maintaining tracking of separating objects after long periods of moving close-by, are presented on PETS 2006, PETS 2009 and EPFL datasets.
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofAdvanced Video and Signal Based Surveillance (AVSS), 2014 11th IEEE International Conference on
dc.rightsrestrictedAccess
dc.titleOcclusion-aware 3D multiple object tracker with two cameras for visual surveillanceen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID35788
dc.contributor.ozuauthorErcan, Ali Özer
dc.identifier.startpage56
dc.identifier.endpage61
dc.identifier.wosWOS:000365607900010
dc.identifier.doi10.1109/AVSS.2014.6918644
dc.subject.keywordsObject tracking
dc.subject.keywordsParticle filtering (numerical methods)
dc.subject.keywordsProbability
dc.subject.keywordsVideo cameras
dc.subject.keywordsVideo surveillance
dc.identifier.scopusSCOPUS:2-s2.0-84909953242
dc.contributor.authorMale1


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