Extraction of moving objects is an important and fundamental research topic for many video applications. This paper addresses an unsupervised spatio-temporal segmentation scheme to extract moving objects from video sequences.The temporal segmentation localizes moving objects by comparing the motion vector of each block in each frame with the corresponding global motion vector estimated by an outlier rejection(OR) based method.Furthermore,the temporal compensation utilizing the temporal coherence of moving objects is considered in the temporal segmentation to solve the temporarily stopping problem.The detected moving regions usually have discontinuous boundaries and some holes.These regions are then compensated in the spatial domain. In the spatial segmentation,the watershed algorithm considering the global information improves the accuracy of segmentation in the spatial domain.The modified mean filter is presented to suppress some minima.By using a fusion module,moving objects are extracted.Experiments on various sequences have successfully demonstrated the validity of the proposed scheme.
Moving object detection in video surveillance is an important step. This paper addresses an automatic object detection algorithm based on spatio-temporal compensation for video surveillance. Temporal difference of the pairs of two frames with a k-frame distance is utilized to obtain coarse object masks. Usually, object regions in these coarse masks have discontinuous boundaries and some holes. Region growing with the distance constraint is proposed to compensate these coarse object regions in spatial domain, followed by filling holes. The added distance constraint can prevent object regions from growing infinitely. The proposed filling holes method is simple and effective. To solve the temporarily stopping problem of moving objects, temporal compensation is proposed to compensate the object mask by utilizing temporal coherence of moving objects in temporal domain. The proposed detection algorithm can extract moving objects as completely as possible. Experimental results have successfully demonstrated the validity of the proposed algorithm.
