Abstract
In the foreground segmentation method based on the Gaussian mixture model, it is assumed that the target required to be detected moves in a image sequence. Hence, when the target stands still, it is learned as a background. After that, when the targets begins to move again, the image region at which the target stood before is apt to be determined as a foreground by mistake. In the proposed method, we examine the movement of the regions detected as a foreground between successive frames, and remove the regions judged to have no movements as an afterimage. Its effectiveness is confirmed through experiments using real image sequences.
