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- Publisher Website: 10.1109/AVSS.2011.6027338
- Scopus: eid_2-s2.0-80053975882
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Conference Paper: An efficient pattern-less background modeling based on scale invariant local states
Title | An efficient pattern-less background modeling based on scale invariant local states |
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Authors | |
Keywords | Background modeling Background pixels Dynamic background Foreground objects Illumination changes |
Issue Date | 2011 |
Publisher | IEEE. 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? |
Abstract | A 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 Identifier | http://hdl.handle.net/10722/137653 |
ISBN | |
References |
DC Field | Value | Language |
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dc.contributor.author | Yuk, JSC | en_HK |
dc.contributor.author | Wong, KYK | en_HK |
dc.date.accessioned | 2011-08-26T14:30:33Z | - |
dc.date.available | 2011-08-26T14:30:33Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.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 | en_HK |
dc.identifier.isbn | 978-1-4577-0845-9 | - |
dc.identifier.uri | http://hdl.handle.net/10722/137653 | - |
dc.description.abstract | A 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.language | eng | en_US |
dc.publisher | IEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001307 | - |
dc.relation.ispartof | Proceedings of the IEEE International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2011 | en_HK |
dc.subject | Background modeling | - |
dc.subject | Background pixels | - |
dc.subject | Dynamic background | - |
dc.subject | Foreground objects | - |
dc.subject | Illumination changes | - |
dc.title | An efficient pattern-less background modeling based on scale invariant local states | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Wong, KYK:kykwong@cs.hku.hk | en_HK |
dc.identifier.authority | Wong, KYK=rp01393 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/AVSS.2011.6027338 | en_HK |
dc.identifier.scopus | eid_2-s2.0-80053975882 | en_HK |
dc.identifier.hkuros | 191683 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-80053975882&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 285 | en_HK |
dc.identifier.epage | 290 | en_HK |
dc.description.other | 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 | - |
dc.identifier.scopusauthorid | Yuk, JSC=18042591200 | en_HK |
dc.identifier.scopusauthorid | Wong, KYK=24402187900 | en_HK |