CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Adaptive epsilon LBP for Background Subtraction
Wang, LF; Wu, HY; Pan, CH
2011
Conference NameCOMPUTER VISION - ACCV 2010, PT III
Source PublicationAsian Conference on Computer Vision (ACCV)
Pages560-571
Conference Date2011
Conference PlaceQueenstown, New Zealand
AbstractBackground subtraction plays an important role in many computer vision systems, yet in complex scenes it is still a challenging task, especially in case of illumination variations. In this work, we develop an efficient texture-based method to tackle this problem. First, we propose a novel adaptive ε LBP operator, in which the threshold is adaptively calculated by compromising two criterions, i.e. the description stability and the discriminative ability. Then, the naive Bayesian technique is adopted to effectively model the probability distribution of local patterns in the pixel level, which utilizes only one single ε LBP pattern instead of ε LBP histogram of local region. Our approach is evaluated on several video sequences against the traditional methods. Experiments show that our method is suitable for various scenes, especially can robust handle illumination variations.
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/4722
Collection模式识别国家重点实验室_先进数据分析与学习
Recommended Citation
GB/T 7714
Wang, LF,Wu, HY,Pan, CH. Adaptive epsilon LBP for Background Subtraction[C],2011:560-571.
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