Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects

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dc.authorid0000-0003-0298-0690
dc.contributor.authorEskil, Mustafa Taneren_US
dc.date.accessioned2015-01-15T23:02:50Z
dc.date.available2015-01-15T23:02:50Z
dc.date.issued2014-10
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractIn particle filtering, dimensionality of the state space can be reduced by tracking control (or feature) points as independent objects, which are traditionally named as partitions. Two critical decisions have to be made in implementation of reduced state-space dimensionality. First is how to construct a dynamic (transition) model for partitions that are inherently dependent. Second critical decision is how to filter partition states such that a viable and likely object state is achieved. In this study, we present a correlation-based transition model and a proposal function that incorporate partition dependency in particle filtering in a computationally tractable manner. We test our algorithm on challenging examples of occlusion, clutter and drastic changes in relative speeds of partitions. Our successful results with as low as 10 particles per partition indicate that the proposed algorithm is both robust and efficient.en_US
dc.description.sponsorshipThis research is part of project "Expression Recognition based on Facial Anatomy", grant number 109E061, supported by The Support Programme for Scientific and Technological Research Projects of The Scientific and Technological Research Council of Turkey (TUBITAK). In comparative evaluation of the tracking algorithms we utilized the SPOT tracking code that was made publicly available by researchers Lu Zhang and Laurens van der Maaten. A special thanks to Fish Species who generously provided the high definition aquarium videos used in our experiments (http://www.fish-species.org.uk)en_US
dc.description.versionPublisher's Versionen_US
dc.description.versionAuthor Post Printen_US
dc.identifier.citationEskil, M. T. (2014). Factored particle filtering with dependent and constrained partition dynamics for tracking deformable objects. Machine Vision and Applications, 25(7), 1825-1840. doi:10.1007/s00138-014-0634-1en_US
dc.identifier.doi10.1007/s00138-014-0634-1
dc.identifier.endpage1840
dc.identifier.issn0932-8092
dc.identifier.issn1432-1769
dc.identifier.issue7
dc.identifier.scopus2-s2.0-84939893462
dc.identifier.scopusqualityQ2
dc.identifier.startpage1825
dc.identifier.urihttps://hdl.handle.net/11729/516
dc.identifier.urihttp://dx.doi.org/10.1007/s00138-014-0634-1
dc.identifier.volume25
dc.identifier.wosWOS:000342435800016
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakScience Citation Index Expanded (SCI-EXPANDED)en_US
dc.institutionauthorEskil, Mustafa Taneren_US
dc.institutionauthorid0000-0003-0298-0690
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringeren_US
dc.relation.ispartofMachine Vision and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParticle filteringen_US
dc.subjectCondensationen_US
dc.subjectFactorized likelihoodsen_US
dc.subjectDeterministic driften_US
dc.subjectProposal functionen_US
dc.subjectMultiple objectsen_US
dc.subjectIntegrationen_US
dc.titleFactored particle filtering with dependent and constrained partition dynamics for tracking deformable objectsen_US
dc.typeArticleen_US
dspace.entity.typePublication

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