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Conference Paper: Human detection in crowded scenes

TitleHuman detection in crowded scenes
Authors
KeywordsHuman detection
Implicit shape model
Occlusions
Issue Date2010
PublisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349
Citation
The 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 721-724 How to Cite?
AbstractIn this paper, our focus is to segment the foreground area for human detection. It is assumed that the foreground region has been detected. Accurate foreground contours are not required. The developed approach adopts a modified ISM (Implicit Shape Model) to collect some typical local patches of human being and their location information. Individuals are detected by grouping some local patches in the foreground area. The method can get good results in crowded scenes. Some examples based on CAVIAR dataset have been shown. A main contribution of the paper is that ISM model and joint occlusion analysis are combined for individual segmentation. There are mainly two advantages: First, with more sufficient information inside the foreground region, even the individuals inside a dense area can also be handled. Secondly, the method does not require an accurate foreground contour. A rough foreground area can be easily obtained in most situations. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/129701
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHou, YLen_HK
dc.contributor.authorPang, GKHen_HK
dc.date.accessioned2010-12-23T08:41:08Z-
dc.date.available2010-12-23T08:41:08Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 721-724en_HK
dc.identifier.issn1522-4880en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129701-
dc.description.abstractIn this paper, our focus is to segment the foreground area for human detection. It is assumed that the foreground region has been detected. Accurate foreground contours are not required. The developed approach adopts a modified ISM (Implicit Shape Model) to collect some typical local patches of human being and their location information. Individuals are detected by grouping some local patches in the foreground area. The method can get good results in crowded scenes. Some examples based on CAVIAR dataset have been shown. A main contribution of the paper is that ISM model and joint occlusion analysis are combined for individual segmentation. There are mainly two advantages: First, with more sufficient information inside the foreground region, even the individuals inside a dense area can also be handled. Secondly, the method does not require an accurate foreground contour. A rough foreground area can be easily obtained in most situations. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherI E E E. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000349en_HK
dc.relation.ispartofProceedings of the 17th IEEE International Conference on Image Processing, ICIP 2010en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Image Processing Proceedings. Copyright © IEEE.-
dc.rights©2010 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.subjectHuman detectionen_HK
dc.subjectImplicit shape modelen_HK
dc.subjectOcclusionsen_HK
dc.titleHuman detection in crowded scenesen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1522-4880&volume=&spage=721&epage=724&date=2010&atitle=Human+detection+in+crowded+scenes-
dc.identifier.emailPang, GKH:gpang@eee.hku.hken_HK
dc.identifier.authorityPang, GKH=rp00162en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICIP.2010.5651982en_HK
dc.identifier.scopuseid_2-s2.0-78651089997en_HK
dc.identifier.hkuros176498en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651089997&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage721en_HK
dc.identifier.epage724en_HK
dc.identifier.isiWOS:000287728000178-
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 17th IEEE International Conference on Image Processing (ICIP 2010), Hong Kong, 26-29 September 2010. In Proceedings of the 17th ICIP, 2010, p. 721-724-
dc.identifier.scopusauthoridHou, YL=25651509000en_HK
dc.identifier.scopusauthoridPang, GKH=7103393283en_HK

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