  Accurate and fast extraction of the foreground object is one of the most
significant issues to be solved due to its important meaning for object
tracking and recognition in video surveillance. Although many foreground object
detection methods have been proposed in the recent past, it is still regarded
as a tough problem due to illumination variations and dynamic backgrounds
challenges. In this paper, we propose a robust foreground object detection
method with two aspects of contributions. First, we propose a robust texture
operator named Robust Local Binary Similarity Pattern (RLBSP), which shows
strong robustness to illumination variations and dynamic backgrounds. Second, a
combination of color and texture features are used to characterize pixel
representations, which compensate each other to make full use of their own
advantages. Comprehensive experiments evaluated on the CDnet 2012 dataset
demonstrate that the proposed method performs favorably against
state-of-the-art methods.
