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 εLBP 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 εLBP pattern

instead of εLBP 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.