Block-based codebook model with oriented-gradient feature for real-time foreground detection

In this paper, a novel approach is proposed to achieve the foreground objects detection in video surveillance system using codebook method. The block-based background model upgrades the pixel-based codebook model to block level which can utilize the dependency and find relationships between neighbouring pixels, thus improving the processing speed and reducing memory during model construction and foreground detection. Moreover, by adding the orientation and magnitude of the block gradient, the codebook model contains not only information of color, but also the texture feature. The texture information can further reduce noises and refine more entire foreground regions. Experimental results prove that our method has better performance compared with the standard codebook and some other former algorithms.