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Article: Scale-adaptive spatial appearance feature density approximation for object tracking

TitleScale-adaptive spatial appearance feature density approximation for object tracking
Authors
KeywordsGaussian mixture model (GMM)
image cue
object appearance representation
tracking
traffic surveillance
Issue Date2011
PublisherI E E E. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.html
Citation
Ieee Transactions On Intelligent Transportation Systems, 2011, v. 12 n. 1, p. 284-290 How to Cite?
AbstractObject tracking is an essential task in visual traffic surveillance. Ideally, a tracker should be able to accurately capture an object's natural motion such as translation, rotation, and scaling. However, it is well known that object appearance varies due to changes in viewing angle, scale, and illumination. They introduce ambiguity to the image cue on which a visual tracker usually relies and which affects the tracking performance. Thus, a robust image appearance cue is required. This paper proposes scale-adaptive spatial appearance feature density approximation to represent objects and construct the image cue. It is found that the appearance representation improves the sensitivity on both the object's rotation and scale. The image cue is then constructed by both the appearance representation of the object and its surrounding background such that distinguishable parts of an object can be tracked under poor imaging conditions. Moreover, tracking dynamics is integrated with the image cue so that objects are efficiently localized in a gradient-based process. Comparative experiments show that the proposed method is effective in capturing the natural motion of objects and generating better tracking accuracy under different image conditions. © 2010 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/137288
ISSN
2015 Impact Factor: 2.534
2015 SCImago Journal Rankings: 1.300
ISI Accession Number ID
Funding AgencyGrant Number
University of Hong Kong
Funding Information:

This work was supported in part by the postgraduate studentship of the University of Hong Kong. The Associate Editor for this paper was M. A. Sotelo Vazquez.

References

 

DC FieldValueLanguage
dc.contributor.authorLiu, CYen_HK
dc.contributor.authorYung, NHCen_HK
dc.date.accessioned2011-08-26T14:22:39Z-
dc.date.available2011-08-26T14:22:39Z-
dc.date.issued2011en_HK
dc.identifier.citationIeee Transactions On Intelligent Transportation Systems, 2011, v. 12 n. 1, p. 284-290en_HK
dc.identifier.issn1524-9050en_HK
dc.identifier.urihttp://hdl.handle.net/10722/137288-
dc.description.abstractObject tracking is an essential task in visual traffic surveillance. Ideally, a tracker should be able to accurately capture an object's natural motion such as translation, rotation, and scaling. However, it is well known that object appearance varies due to changes in viewing angle, scale, and illumination. They introduce ambiguity to the image cue on which a visual tracker usually relies and which affects the tracking performance. Thus, a robust image appearance cue is required. This paper proposes scale-adaptive spatial appearance feature density approximation to represent objects and construct the image cue. It is found that the appearance representation improves the sensitivity on both the object's rotation and scale. The image cue is then constructed by both the appearance representation of the object and its surrounding background such that distinguishable parts of an object can be tracked under poor imaging conditions. Moreover, tracking dynamics is integrated with the image cue so that objects are efficiently localized in a gradient-based process. Comparative experiments show that the proposed method is effective in capturing the natural motion of objects and generating better tracking accuracy under different image conditions. © 2010 IEEE.en_HK
dc.languageengen_US
dc.publisherI E E E. 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.rightsIEEE Transactions on Intelligent Transportation Systems. Copyright © IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rights©2011 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.subjectGaussian mixture model (GMM)en_HK
dc.subjectimage cueen_HK
dc.subjectobject appearance representationen_HK
dc.subjecttrackingen_HK
dc.subjecttraffic surveillanceen_HK
dc.titleScale-adaptive spatial appearance feature density approximation for object trackingen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1524-9050&volume=12&issue=1&spage=284&epage=290&date=2011&atitle=Scale-adaptive+spatial+appearance+feature+density+approximation+method+for+object+tracking-
dc.identifier.emailYung, NHC:nyung@eee.hku.hken_HK
dc.identifier.authorityYung, NHC=rp00226en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TITS.2010.2090871en_HK
dc.identifier.scopuseid_2-s2.0-79952072338en_HK
dc.identifier.hkuros190969en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79952072338&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume12en_HK
dc.identifier.issue1en_HK
dc.identifier.spage284en_HK
dc.identifier.epage290en_HK
dc.identifier.isiWOS:000287867000027-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLiu, CY=26431035900en_HK
dc.identifier.scopusauthoridYung, NHC=7003473369en_HK

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