Stationary foreground detection for video-surveillance based on foreground and motion history images

Stationary foreground detection is a common stage in many video-surveillance applications. In this paper, we propose an approach for stationary foreground detection in video based on the spatio-temporal variation of foreground and motion data. Foreground data are obtained by Background Subtraction to detect regions of interest. Motion data allows to filter out the moving regions and it is estimated using median filters over sliding windows. Spatiotemporal patterns of both data are computed through history images and the final detection is obtained using a two-threshold scheme that considers motion activity. Partial visibility of stationary foreground for short-time intervals is handled to increase robustness. The results over challenging video-surveillance sequences show an improvement of the proposed approach against the related work.