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Article: Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field
Title | Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field |
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Authors | |
Keywords | Background reconstruction Conditional random fields Mixed-state Markov models Motion detection |
Issue Date | 2011 |
Publisher | Springer 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? |
Abstract | In 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 Identifier | http://hdl.handle.net/10722/135513 |
ISSN | 2023 Impact Factor: 11.6 2023 SCImago Journal Rankings: 6.668 |
ISI Accession Number ID | |
References |
DC Field | Value | Language |
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dc.contributor.author | Crivelli, T | en_HK |
dc.contributor.author | Bouthemy, P | en_HK |
dc.contributor.author | CernuschiFrías, B | en_HK |
dc.contributor.author | Yao, JF | en_HK |
dc.date.accessioned | 2011-07-27T01:36:14Z | - |
dc.date.available | 2011-07-27T01:36:14Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | International Journal Of Computer Vision, 2011, v. 94 n. 3, p. 295-316 | en_HK |
dc.identifier.issn | 0920-5691 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/135513 | - |
dc.description.abstract | In 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.language | eng | en_US |
dc.publisher | Springer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0920-5691 | en_HK |
dc.relation.ispartof | International Journal of Computer Vision | en_HK |
dc.rights | The original publication is available at www.springerlink.com | en_US |
dc.subject | Background reconstruction | en_HK |
dc.subject | Conditional random fields | en_HK |
dc.subject | Mixed-state Markov models | en_HK |
dc.subject | Motion detection | en_HK |
dc.title | Simultaneous motion detection and background reconstruction with a conditional mixed-state markov random field | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Yao, JF: jeffyao@hku.hk | en_HK |
dc.identifier.authority | Yao, JF=rp01473 | en_HK |
dc.description.nature | postprint | - |
dc.identifier.doi | 10.1007/s11263-011-0429-z | en_HK |
dc.identifier.scopus | eid_2-s2.0-79960250078 | en_HK |
dc.identifier.hkuros | 187968 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-79960250078&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 94 | en_HK |
dc.identifier.issue | 3 | en_HK |
dc.identifier.spage | 295 | en_HK |
dc.identifier.epage | 316 | en_HK |
dc.identifier.eissn | 1573-1405 | - |
dc.identifier.isi | WOS:000294570100003 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Crivelli, T=25824832900 | en_HK |
dc.identifier.scopusauthorid | Bouthemy, P=7005146506 | en_HK |
dc.identifier.scopusauthorid | CernuschiFrías, B=7003558300 | en_HK |
dc.identifier.scopusauthorid | Yao, JF=7403503451 | en_HK |
dc.identifier.citeulike | 9048927 | - |
dc.identifier.issnl | 0920-5691 | - |