Temporarily static object detection in surveillance video using double foregrounds and superpixels

In surveillance video applications, temporarily static regions indicate move-then-stop objects, such as the abandoned/removed objects, parked vehicles. This paper presents an approach using both pixel-level and region-level analysis together. In the pixel-level foreground extraction process, two improved Gaussian Mixture Model(GMM) are adopted to obtain the binary foreground mask. A residence map is also set to measure the lasting time for an abandoned/removed object. In the region-level analysis, we apply a superpixel-based method, which could refine the foreground extraction result and further generate the output of exact object regions instead of only bounding boxes in other related works. Experimental results show the proposed method could detect the temporarily static objects effectively and accurately.