Circulant structures based moving object detection

Background modeling and subtraction, the task to detect moving objects in a scene, is a fundamental and critical step for many high level computer vision tasks. However, background subtraction modeling is still an open and challenge problem particularly in practical scenarios with drastic illumination changes and dynamic backgrounds. In this paper, we present a novel background modeling method focused on dealing with complex environments based on circular shift operator. Firstly, the neighborhood of each pixel forming a region as a basis unit, performing circular shift on this unit to construct the background model. Then subtract the established background from the current frame to obtain the distance map. Thirdly, adopt the graph-cut on the distance map to perform foreground detection. The background model is updated with an adaptive update rate. Experimental results on indoor and outdoor videos demonstrate the efficiency of our proposed method.