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Article: Scale-adaptive spatial appearance feature density approximation for object tracking
Title | Scale-adaptive spatial appearance feature density approximation for object tracking | ||||
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Authors | |||||
Keywords | Gaussian mixture model (GMM) image cue object appearance representation tracking traffic surveillance | ||||
Issue Date | 2011 | ||||
Publisher | I 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? | ||||
Abstract | Object 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 Identifier | http://hdl.handle.net/10722/137288 | ||||
ISSN | 2023 Impact Factor: 7.9 2023 SCImago Journal Rankings: 2.580 | ||||
ISI Accession Number ID |
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 Field | Value | Language |
---|---|---|
dc.contributor.author | Liu, CY | en_HK |
dc.contributor.author | Yung, NHC | en_HK |
dc.date.accessioned | 2011-08-26T14:22:39Z | - |
dc.date.available | 2011-08-26T14:22:39Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | Ieee Transactions On Intelligent Transportation Systems, 2011, v. 12 n. 1, p. 284-290 | en_HK |
dc.identifier.issn | 1524-9050 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/137288 | - |
dc.description.abstract | Object 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.language | eng | en_US |
dc.publisher | I E E E. The Journal's web site is located at http://www.ewh.ieee.org/tc/its/trans.html | en_HK |
dc.relation.ispartof | IEEE Transactions on Intelligent Transportation Systems | en_HK |
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.subject | Gaussian mixture model (GMM) | en_HK |
dc.subject | image cue | en_HK |
dc.subject | object appearance representation | en_HK |
dc.subject | tracking | en_HK |
dc.subject | traffic surveillance | en_HK |
dc.title | Scale-adaptive spatial appearance feature density approximation for object tracking | en_HK |
dc.type | Article | en_HK |
dc.identifier.openurl | http://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.email | Yung, NHC:nyung@eee.hku.hk | en_HK |
dc.identifier.authority | Yung, NHC=rp00226 | en_HK |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.1109/TITS.2010.2090871 | en_HK |
dc.identifier.scopus | eid_2-s2.0-79952072338 | en_HK |
dc.identifier.hkuros | 190969 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-79952072338&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 12 | en_HK |
dc.identifier.issue | 1 | en_HK |
dc.identifier.spage | 284 | en_HK |
dc.identifier.epage | 290 | en_HK |
dc.identifier.isi | WOS:000287867000027 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Liu, CY=26431035900 | en_HK |
dc.identifier.scopusauthorid | Yung, NHC=7003473369 | en_HK |
dc.identifier.issnl | 1524-9050 | - |