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Conference Paper: Shadow detection for vehicles by locating the object-shadow boundary

TitleShadow detection for vehicles by locating the object-shadow boundary
Authors
KeywordsObject detection
Shadow detection
Shadow identification
Video segmentation
Issue Date2005
Citation
Proceedings Of The Seventh Iasted International Conference On Signal And Image Processing, Sip 2005, 2005, p. 315-319 How to Cite?
AbstractWe introduce in this paper a shadow detection method for vehicles in traffic video sequences. Our method approximates the boundary between vehicles and their associated shadows by one or more straight lines. These lines are located in the image by exploiting both local information (e.g. statistics in intensity differences) and global information (e.g. principal edge directions). The proposed method does not assume a particular lighting condition, and requires no human interaction nor parameter training. Experiments on practical real-world traffic video sequences demonstrate that our method is simple, robust and efficient under traffic scenes with different lighting conditions. Accurate positioning of target vehicles is thus achieved even in the presence of cast shadows.
Persistent Identifierhttp://hdl.handle.net/10722/93382
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorSo, AWKen_HK
dc.contributor.authorWong, KYKen_HK
dc.contributor.authorChung, RHYen_HK
dc.contributor.authorChin, FYLen_HK
dc.date.accessioned2010-09-25T14:59:23Z-
dc.date.available2010-09-25T14:59:23Z-
dc.date.issued2005en_HK
dc.identifier.citationProceedings Of The Seventh Iasted International Conference On Signal And Image Processing, Sip 2005, 2005, p. 315-319en_HK
dc.identifier.isbn0889865183-
dc.identifier.urihttp://hdl.handle.net/10722/93382-
dc.description.abstractWe introduce in this paper a shadow detection method for vehicles in traffic video sequences. Our method approximates the boundary between vehicles and their associated shadows by one or more straight lines. These lines are located in the image by exploiting both local information (e.g. statistics in intensity differences) and global information (e.g. principal edge directions). The proposed method does not assume a particular lighting condition, and requires no human interaction nor parameter training. Experiments on practical real-world traffic video sequences demonstrate that our method is simple, robust and efficient under traffic scenes with different lighting conditions. Accurate positioning of target vehicles is thus achieved even in the presence of cast shadows.en_HK
dc.languageengen_HK
dc.relation.ispartofProceedings of the Seventh IASTED International Conference on Signal and Image Processing, SIP 2005en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectObject detectionen_HK
dc.subjectShadow detectionen_HK
dc.subjectShadow identificationen_HK
dc.subjectVideo segmentationen_HK
dc.titleShadow detection for vehicles by locating the object-shadow boundaryen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0889865183 (ISBN)&volume=&spage=315&epage=319&date=2005&atitle=Shadow+detection+for+vehicles+by+locating+the+object-shadow+boundary-
dc.identifier.emailWong, KYK:kykwong@cs.hku.hken_HK
dc.identifier.emailChung, RHY:hychung@cs.hku.hken_HK
dc.identifier.emailChin, FYL:chin@cs.hku.hken_HK
dc.identifier.authorityWong, KYK=rp01393en_HK
dc.identifier.authorityChung, RHY=rp00219en_HK
dc.identifier.authorityChin, FYL=rp00105en_HK
dc.description.naturepostprint-
dc.identifier.scopuseid_2-s2.0-33644508385en_HK
dc.identifier.hkuros102864en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33644508385&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage315en_HK
dc.identifier.epage319en_HK
dc.identifier.scopusauthoridSo, AWK=12754102400en_HK
dc.identifier.scopusauthoridWong, KYK=24402187900en_HK
dc.identifier.scopusauthoridChung, RHY=14059962600en_HK
dc.identifier.scopusauthoridChin, FYL=7005101915en_HK

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