HIERARCHICAL CODEBOOK BACKGROUND MODEL USING HAAR-LIKE FEATURES

Background subtraction is one of the most popular methods to detect moving objects in videos. In this paper, we propose an efficient hierarchical background subtraction method with block-based and pixel-based codebooks (CBs) using haar-like features for foreground detection. In the block-based stage, four haar-like features and a block average value, which can be calculated rapidly using integral image and are not sensitive to dynamic background, are used to represent a block. Through the block-based stage we can remove most of the background without reducing the true positive rate. To overcome the low precision problem in the block-based stage, the pixel-based stage is adopted to increase the precision. Experiment results show that our approach can provide faster computation speed compared with that of the present related approaches meanwhile, ensure a high correct detection rate.