Segmentation of Moving Foreground Objects Using Codebook and Local Binary Patterns

Robust detection of moving objects in complex scenes is one of the most challenging issues in computer vision. In this paper, we present a novel texture-wise approach to segment moving objects with codebook and local binary patterns (LBP). In many moving segmentation algorithms, the information from limited frames before current image is used. Our approach models background over long time with small memory. Firstly, we construct codebook model which represents a compressed form of background model for long image sequences. A single Gaussian model of per-pixel is built to deal with illumination changes. By using the correlation and texture of spatially proximal pixels, local binary patterns background model is constructed. Finally current image is segmented into two parts, foreground and background, by comparing current image with background model. Experiments show that the proposed approach achieves promising results robustly in real videos.