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Conference Paper: Vehicle shape approximation from motion for visual traffic surveillance

TitleVehicle shape approximation from motion for visual traffic surveillance
Authors
KeywordsTransportation
Issue Date2001
PublisherIEEE.
Citation
Ieee Conference On Intelligent Transportation Systems, Proceedings, Itsc, 2001, p. 608-613 How to Cite?
AbstractIn this paper, a vehicle shape approximation method based on the vehicle motion in a typical traffic image sequence is proposed. In the proposed method, instead of using the 2D image data directly, the intrinsic 3D data is estimated in a monocular image sequence. Given the binary vehicle mask and the camera parameters, the vehicle shape is estimated by the four stages shape approximation method. These stages include feature point extraction, feature point motion estimation between two consecutive frames, feature point height estimation from motion vector, and the 3D shape estimation based on the feature point height. We have tested our method using real world traffic image sequence and the vehicle height profile and dimensions are estimated to be reasonably close to the actual dimensions.
Persistent Identifierhttp://hdl.handle.net/10722/46323
References

 

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dc.contributor.authorFung, GSKen_HK
dc.contributor.authorYung, NHCen_HK
dc.contributor.authorPang, GKHen_HK
dc.date.accessioned2007-10-30T06:47:20Z-
dc.date.available2007-10-30T06:47:20Z-
dc.date.issued2001en_HK
dc.identifier.citationIeee Conference On Intelligent Transportation Systems, Proceedings, Itsc, 2001, p. 608-613en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46323-
dc.description.abstractIn this paper, a vehicle shape approximation method based on the vehicle motion in a typical traffic image sequence is proposed. In the proposed method, instead of using the 2D image data directly, the intrinsic 3D data is estimated in a monocular image sequence. Given the binary vehicle mask and the camera parameters, the vehicle shape is estimated by the four stages shape approximation method. These stages include feature point extraction, feature point motion estimation between two consecutive frames, feature point height estimation from motion vector, and the 3D shape estimation based on the feature point height. We have tested our method using real world traffic image sequence and the vehicle height profile and dimensions are estimated to be reasonably close to the actual dimensions.en_HK
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dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSCen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2001 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.en_HK
dc.subjectTransportationen_HK
dc.titleVehicle shape approximation from motion for visual traffic surveillanceen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ITSC.2001.948729en_HK
dc.identifier.scopuseid_2-s2.0-0034784237en_HK
dc.identifier.hkuros72322-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034784237&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage608en_HK
dc.identifier.epage613en_HK
dc.identifier.scopusauthoridFung, GSK=7004213392en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK

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