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Conference Paper: Vehicle type classification from visual-based dimension estimation

TitleVehicle type classification from visual-based dimension estimation
Authors
KeywordsCamera calibration
Dimension estimation
Shadow removal
Vehicle modeling
Issue Date2001
PublisherIEEE.
Citation
Ieee Conference On Intelligent Transportation Systems, Proceedings, Itsc, 2001, p. 201-206 How to Cite?
AbstractThis paper presents a visual-based dimension estimation method for vehicle type classification. Our method extracts moving vehicles from traffic image sequences and fits them with a simple deformable vehicle model. Using a set of coordination mapping functions derived from a calibrated camera model and relying on a shadow removal method, vehicle's width, length and height are estimated. Our experimental tests show that the modeling method is effective and the estimation accuracy is sufficient for general vehicle type classification.
Persistent Identifierhttp://hdl.handle.net/10722/46322
References

 

DC FieldValueLanguage
dc.contributor.authorLai, AHSen_HK
dc.contributor.authorFung, GSKen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2007-10-30T06:47:19Z-
dc.date.available2007-10-30T06:47:19Z-
dc.date.issued2001en_HK
dc.identifier.citationIeee Conference On Intelligent Transportation Systems, Proceedings, Itsc, 2001, p. 201-206en_HK
dc.identifier.urihttp://hdl.handle.net/10722/46322-
dc.description.abstractThis paper presents a visual-based dimension estimation method for vehicle type classification. Our method extracts moving vehicles from traffic image sequences and fits them with a simple deformable vehicle model. Using a set of coordination mapping functions derived from a calibrated camera model and relying on a shadow removal method, vehicle's width, length and height are estimated. Our experimental tests show that the modeling method is effective and the estimation accuracy is sufficient for general vehicle type classification.en_HK
dc.format.extent668072 bytes-
dc.format.extent1995 bytes-
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dc.format.extent10863 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSCen_HK
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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectCamera calibrationen_HK
dc.subjectDimension estimationen_HK
dc.subjectShadow removalen_HK
dc.subjectVehicle modelingen_HK
dc.titleVehicle type classification from visual-based dimension estimationen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/ITSC.2001.948656en_HK
dc.identifier.scopuseid_2-s2.0-0034779060en_HK
dc.identifier.hkuros72318-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0034779060&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage201en_HK
dc.identifier.epage206en_HK
dc.identifier.scopusauthoridLai, AHS=7102225794en_HK
dc.identifier.scopusauthoridFung, GSK=7004213392en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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