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Conference Paper: An efficient pattern-less background modeling based on scale invariant local states

TitleAn efficient pattern-less background modeling based on scale invariant local states
Authors
KeywordsBackground modeling
Background pixels
Dynamic background
Foreground objects
Illumination changes
Issue Date2011
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001307
Citation
The 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2011), Klagenfurt, Austria, 30 Auguist-2 September 2011. In Proceedings of 8th AVSS, 2011, p. 285-290 How to Cite?
AbstractA robust and efficient background modeling algorithm is crucial to the success of most of the intelligent video surveillance systems. Compared with intensity-based approaches, texture-based background modeling approaches have shown to be more robust against dynamic backgrounds and illumination changes, which are common in real life videos. However, many of the existing texture-based methods are too computationally expensive, which renders them useless in real-time applications. In this paper, a novel efficient texture-based background modeling algorithm is presented. Scale invariant local states (SILS) are introduced as pixel features for modeling a background pixel, and a pattern-less probabilistic measurement (PLPM) is derived to estimate the probability of a pixel being background from its SILS. An adaptive background modeling framework is also introduced for learning and representing a multi-modal background model. Experimental results show that the proposed method can run nearly 3 times faster than existing state-of-the-art texture-based method, without sacrificing the output quality. This allows more time for a real-time surveillance system to carry out other computationally intensive analysis on the detected foreground objects. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/137653
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorYuk, JSCen_HK
dc.contributor.authorWong, KYKen_HK
dc.date.accessioned2011-08-26T14:30:33Z-
dc.date.available2011-08-26T14:30:33Z-
dc.date.issued2011en_HK
dc.identifier.citationThe 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2011), Klagenfurt, Austria, 30 Auguist-2 September 2011. In Proceedings of 8th AVSS, 2011, p. 285-290en_HK
dc.identifier.isbn978-1-4577-0845-9-
dc.identifier.urihttp://hdl.handle.net/10722/137653-
dc.description.abstractA robust and efficient background modeling algorithm is crucial to the success of most of the intelligent video surveillance systems. Compared with intensity-based approaches, texture-based background modeling approaches have shown to be more robust against dynamic backgrounds and illumination changes, which are common in real life videos. However, many of the existing texture-based methods are too computationally expensive, which renders them useless in real-time applications. In this paper, a novel efficient texture-based background modeling algorithm is presented. Scale invariant local states (SILS) are introduced as pixel features for modeling a background pixel, and a pattern-less probabilistic measurement (PLPM) is derived to estimate the probability of a pixel being background from its SILS. An adaptive background modeling framework is also introduced for learning and representing a multi-modal background model. Experimental results show that the proposed method can run nearly 3 times faster than existing state-of-the-art texture-based method, without sacrificing the output quality. This allows more time for a real-time surveillance system to carry out other computationally intensive analysis on the detected foreground objects. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001307-
dc.relation.ispartofProceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2011en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsProceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance. Copyright © IEEE.-
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.subjectBackground modeling-
dc.subjectBackground pixels-
dc.subjectDynamic background-
dc.subjectForeground objects-
dc.subjectIllumination changes-
dc.titleAn efficient pattern-less background modeling based on scale invariant local statesen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailWong, KYK:kykwong@cs.hku.hken_HK
dc.identifier.authorityWong, KYK=rp01393en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/AVSS.2011.6027338en_HK
dc.identifier.scopuseid_2-s2.0-80053975882en_HK
dc.identifier.hkuros191683en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80053975882&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage285en_HK
dc.identifier.epage290en_HK
dc.description.otherThe 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2011), Klagenfurt, Austria, 30 Auguist-2 September 2011. In Proceedings of 8th AVSS, 2011, p. 285-290-
dc.identifier.scopusauthoridYuk, JSC=18042591200en_HK
dc.identifier.scopusauthoridWong, KYK=24402187900en_HK

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