Abstract
Background 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.
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Wang, L., Wu, H., Pan, C. (2011). Adaptive ε LBP for Background Subtraction. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6494. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19318-7_44
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DOI: https://doi.org/10.1007/978-3-642-19318-7_44
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