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This study presents a pixel-wise statistical approach to distinguish every pixel into foreground, background, shadow or highlight for background subtraction and foreground detection. The modified RGB color model is proposed to effectively reduce misclassifying the foreground pixels into highlights. The modified Highest Redundancy Ratio (HRR)-based background update method is also proposed to overcome the lighting variation and slow motion object problems in background reconstruction. In tracking procedure, a decision function with low computational complexity is proposed to sequentially evaluate the objectspsila correlation between consecutive frames. The decision function consists of the objectspsila centroid distances, objectspsila area differences, and objectspsila overlapping areas between current frame and previous frame. As documented in experimental results, the proposed method can achieves high matching rate, which is great advantageous in surveillance systems.