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Article: A method for vehicle count in the presence of multiple-vehicle occlusions in traffic images

TitleA method for vehicle count in the presence of multiple-vehicle occlusions in traffic images
Authors
KeywordsDeformable model
Occlusion
Occlusion reasoning
Resolvability
Vehicle counting
Vehicle segmentation
Vehicle tracking
Issue Date2007
PublisherIEEE. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.html
Citation
Ieee Transactions On Intelligent Transportation Systems, 2007, v. 8 n. 3, p. 441-459 How to Cite?
AbstractThis paper proposes a novel method for accurately counting the number of vehicles that are involved in multiple-vehicle occlusions, based on the resolvability of each occluded vehicle, as seen in a monocular traffic image sequence. Assuming that the occluded vehicles are segmented from the road background by a previously proposed vehicle segmentation method and that a deformable model is geometrically fitted onto the occluded vehicles, the proposed method first deduces the number of vertices per individual vehicle from the camera configuration. Second, a contour description model is utilized to describe the direction of the contour segments with respect to its vanishing points, from which individual contour description and vehicle count are determined. Third, it assigns a resolvability index to each occluded vehicle based on a resolvability model, from which each occluded vehicle model is resolved and the vehicle dimension is measured. The proposed method has been tested on 267 sets of real-world monocular traffic images containing 3074 vehicles with multiple-vehicle occlusions and is found to be 100% accurate in calculating vehicle count, in comparison with human inspection. By comparing the estimated dimensions of the resolved generalized deformable model of the vehicle with the actual dimensions published by the manufacturers, the root-mean-square error for width, length, and height estimations are found to be 48, 279, and 76 mm, respectively. © 2007 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/57488
ISSN
2015 Impact Factor: 2.534
2015 SCImago Journal Rankings: 1.300
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorPang, CCCen_HK
dc.contributor.authorLam, WWLen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2010-04-12T01:37:51Z-
dc.date.available2010-04-12T01:37:51Z-
dc.date.issued2007en_HK
dc.identifier.citationIeee Transactions On Intelligent Transportation Systems, 2007, v. 8 n. 3, p. 441-459en_HK
dc.identifier.issn1524-9050en_HK
dc.identifier.urihttp://hdl.handle.net/10722/57488-
dc.description.abstractThis paper proposes a novel method for accurately counting the number of vehicles that are involved in multiple-vehicle occlusions, based on the resolvability of each occluded vehicle, as seen in a monocular traffic image sequence. Assuming that the occluded vehicles are segmented from the road background by a previously proposed vehicle segmentation method and that a deformable model is geometrically fitted onto the occluded vehicles, the proposed method first deduces the number of vertices per individual vehicle from the camera configuration. Second, a contour description model is utilized to describe the direction of the contour segments with respect to its vanishing points, from which individual contour description and vehicle count are determined. Third, it assigns a resolvability index to each occluded vehicle based on a resolvability model, from which each occluded vehicle model is resolved and the vehicle dimension is measured. The proposed method has been tested on 267 sets of real-world monocular traffic images containing 3074 vehicles with multiple-vehicle occlusions and is found to be 100% accurate in calculating vehicle count, in comparison with human inspection. By comparing the estimated dimensions of the resolved generalized deformable model of the vehicle with the actual dimensions published by the manufacturers, the root-mean-square error for width, length, and height estimations are found to be 48, 279, and 76 mm, respectively. © 2007 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.htmlen_HK
dc.relation.ispartofIEEE Transactions on Intelligent Transportation Systemsen_HK
dc.rights©2007 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.subjectDeformable modelen_HK
dc.subjectOcclusionen_HK
dc.subjectOcclusion reasoningen_HK
dc.subjectResolvabilityen_HK
dc.subjectVehicle countingen_HK
dc.subjectVehicle segmentationen_HK
dc.subjectVehicle trackingen_HK
dc.titleA method for vehicle count in the presence of multiple-vehicle occlusions in traffic imagesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1524-9050&volume=8&issue=3&spage=441&epage=459&date=2007&atitle=A+method+for+vehicle+count+in+the+presence+of+multiple-vehicle+occlusions+in+traffic+imagesen_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/TITS.2007.902647en_HK
dc.identifier.scopuseid_2-s2.0-34548525509en_HK
dc.identifier.hkuros143214-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548525509&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume8en_HK
dc.identifier.issue3en_HK
dc.identifier.spage441en_HK
dc.identifier.epage459en_HK
dc.identifier.isiWOS:000249403800008-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridPang, CCC=7201425202en_HK
dc.identifier.scopusauthoridLam, WWL=16836339900en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK
dc.identifier.citeulike6462650-

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