Moving-object segmentation using a foreground history map.

This paper describes a real-time foreground segmentation method in monocular video sequences for video teleconferencing. Background subtraction is widely used in foreground segmentation for static cameras. However, the results are usually not accurate enough for background substitution tasks. In this paper, we propose a novel strategy for fast and accurate foreground segmentation. The strategy consists of two steps: initial foreground segmentation and fine foreground segmentation. The key to our algorithm consists of two steps. In the first step, a moving object is roughly segmented using the background subtraction method. In order to update the initial foreground segmentation results in the second step, a region-based segmentation method and a foreground history map (FHM)-based segmentation representing the combination of temporal and spatial information were developed. The segmentation accuracy of the proposed algorithm was evaluated with respect to the ground truth, which was the manually cropped foreground. The experimental results showed that the proposed algorithm improved the accuracy of segmentation with respect to Horprasert's well-known algorithm.