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Conference Paper: Particle Filter for Targets Tracking with Motion Model

TitleParticle Filter for Targets Tracking with Motion Model
Authors
Keywordstarget tracking
particle filter
kernel density estimation
Issue Date2013
PublisherI E E E.
Citation
The 8th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 17-20 December 2013. In International Conference on Industrial and Information Systems, 2013, p. 128-132, article no. 6731968 How to Cite?
AbstractReal-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.
Persistent Identifierhttp://hdl.handle.net/10722/203994
ISBN

 

DC FieldValueLanguage
dc.contributor.authorPang, GKHen_US
dc.contributor.authorChoy, KLen_US
dc.date.accessioned2014-09-19T20:01:27Z-
dc.date.available2014-09-19T20:01:27Z-
dc.date.issued2013en_US
dc.identifier.citationThe 8th International Conference on Industrial and Information Systems (ICIIS), Peradeniya, Sri Lanka, 17-20 December 2013. In International Conference on Industrial and Information Systems, 2013, p. 128-132, article no. 6731968en_US
dc.identifier.isbn9781479909100-
dc.identifier.urihttp://hdl.handle.net/10722/203994-
dc.description.abstractReal-time robust tracking for multiple non-rigid objects is a challenging task in computer vision research. In recent years, stochastic sampling based particle filter has been widely used to describe the complicated target features of image sequence. In this paper, non-parametric density estimation and particle filter techniques are employed to model the background and track the object. Color feature and motion model of the target are extracted and used as key features in the tracking step, in order to adapt to multiple variations in the scene, such as background clutters, object's scale change and partial overlap of different targets. The paper also presents the experimental result on the robustness and effectiveness of the proposed method in a number of outdoor and indoor visual surveillance scenes.-
dc.languageengen_US
dc.publisherI E E E.-
dc.relation.ispartofInternational Conference on Industrial and Information Systemsen_US
dc.rightsInternational Conference on Industrial and Information Systems. Copyright © I E E E.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjecttarget tracking-
dc.subjectparticle filter-
dc.subjectkernel density estimation-
dc.titleParticle Filter for Targets Tracking with Motion Modelen_US
dc.typeConference_Paperen_US
dc.identifier.emailPang, GKH: gpang@eee.hku.hken_US
dc.identifier.authorityPang, GKH=rp00162en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIInfS.2013.6731968-
dc.identifier.scopuseid_2-s2.0-84894435101-
dc.identifier.hkuros236051en_US
dc.identifier.spage128, article no. 6731968en_US
dc.identifier.epage132, article no. 6731968en_US
dc.publisher.placeUnited States-

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