CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Fast and Effective Background Subtraction Based on eLBP
Wang, Lingfeng; Pan, Chunhong
2010
Conference NameInternational Conference on Acoustics,Speech, and Signal Processing
Source PublicationInternational Conference on Acoustics,Speech, and Signal Processing
Conference Date2010
Conference PlaceDallas, Texas, USA
AbstractBackground subtraction is an important component of many com-puter vision systems. In this paper, we present a fast and efficient texture-based method for background subtraction in a video se-quence. At first, a novel LBP (Local Binary Pattern) operator called εLBP is applied to extract the local description of each pixel. Then, the kernel of background subtraction is performed based on two principle phases, i.e. the background building phase and the fore-ground detection phase. In the former phase, the probability that a pixel belongs to foreground is calculated based on the difference between the current εLBP and the adaptive mean εLBP. In the later phase, a thresholding operator is applied on the probability to determine whether a pixel can be classified as foreground, and the adaptive mean εLBP is updated by a user-settable learning rate. Finally, our approach is evaluated against several video sequences compared with the traditional MoG model. Experiments show that our method is suitable for various scenes and is appealing with respect to robustness.
KeywordBackground Subtraction ε Lbp
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/10766
Collection模式识别国家重点实验室_先进数据分析与学习
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Wang, Lingfeng,Pan, Chunhong. Fast and Effective Background Subtraction Based on eLBP[C],2010.
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