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Article: Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field

TitleSimultaneous motion detection and background reconstruction with a conditional mixed-state markov random field
Authors
KeywordsBackground reconstruction
Conditional random fields
Mixed-state Markov models
Motion detection
Issue Date2011
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0920-5691
Citation
International Journal Of Computer Vision, 2011, v. 94 n. 3, p. 295-316 How to Cite?
AbstractIn this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed. © 2011 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/135513
ISSN
2015 Impact Factor: 4.27
2015 SCImago Journal Rankings: 5.633
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorCrivelli, Ten_HK
dc.contributor.authorBouthemy, Pen_HK
dc.contributor.authorCernuschiFrías, Ben_HK
dc.contributor.authorYao, JFen_HK
dc.date.accessioned2011-07-27T01:36:14Z-
dc.date.available2011-07-27T01:36:14Z-
dc.date.issued2011en_HK
dc.identifier.citationInternational Journal Of Computer Vision, 2011, v. 94 n. 3, p. 295-316en_HK
dc.identifier.issn0920-5691en_HK
dc.identifier.urihttp://hdl.handle.net/10722/135513-
dc.description.abstractIn this work we present a new way of simultaneously solving the problems of motion detection and background image reconstruction. An accurate estimation of the background is only possible if we locate the moving objects. Meanwhile, a correct motion detection is achieved if we have a good available background model. The key of our joint approach is to define a single random process that can take two types of values, instead of defining two different processes, one symbolic (motion detection) and one numeric (background intensity estimation). It thus allows to exploit the (spatio-temporal) interaction between a decision (motion detection) and an estimation (intensity reconstruction) problem. Consequently, the meaning of solving both tasks jointly, is to obtain a single optimal estimate of such a process. The intrinsic interaction and simultaneity between both problems is shown to be better modeled within the so-called mixed-state statistical framework, which is extended here to account for symbolic states and conditional random fields. Experiments on real sequences and comparisons with existing motion detection methods support our proposal. Further implications for video sequence inpainting will be also discussed. © 2011 Springer Science+Business Media, LLC.en_HK
dc.languageengen_US
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0920-5691en_HK
dc.relation.ispartofInternational Journal of Computer Visionen_HK
dc.rightsThe original publication is available at www.springerlink.comen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBackground reconstructionen_HK
dc.subjectConditional random fieldsen_HK
dc.subjectMixed-state Markov modelsen_HK
dc.subjectMotion detectionen_HK
dc.titleSimultaneous motion detection and background reconstruction with a conditional mixed-state markov random fielden_HK
dc.typeArticleen_HK
dc.identifier.emailYao, JF: jeffyao@hku.hken_HK
dc.identifier.authorityYao, JF=rp01473en_HK
dc.description.naturepostprint-
dc.identifier.doi10.1007/s11263-011-0429-zen_HK
dc.identifier.scopuseid_2-s2.0-79960250078en_HK
dc.identifier.hkuros187968en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-79960250078&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume94en_HK
dc.identifier.issue3en_HK
dc.identifier.spage295en_HK
dc.identifier.epage316en_HK
dc.identifier.eissn1573-1405-
dc.identifier.isiWOS:000294570100003-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridCrivelli, T=25824832900en_HK
dc.identifier.scopusauthoridBouthemy, P=7005146506en_HK
dc.identifier.scopusauthoridCernuschiFrías, B=7003558300en_HK
dc.identifier.scopusauthoridYao, JF=7403503451en_HK
dc.identifier.citeulike9048927-

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