Efficient Spatio-temporal Segmentation for Extracting Moving Objects in Video Sequences
Extraction of moving objects is an important and fundamental research topic for many digital video applications. This paper addresses an efficient spatio-temporal segmentation scheme to extract moving objects from video sequences. The temporal segmentation yields a temporal mask that indicates moving regions and static regions for each frame. For localization of moving objects, a block-based motion detection method considering a novel feature measure is proposed to detect changed regions. These changed regions are coarse and need accurate spatial compensation. An edge-based morphological dilation method is presented to achieve the anisotropic expansion of the changed regions. Furthermore, to solve the temporarily stopping problem of moving objects, the inertia information of moving objects is considered in the temporal segmentation. The spatial segmentation based on the watershed algorithm is performed to provide homogeneous regions with closed and precise boundaries. It considers the global information to improve the accuracy of the boundaries. To reduce over-segmentation in the watershed segmentation, a novel mean filter is proposed to suppress some minima. A fusion of the spatial and temporal segmentation results produces complete moving objects faithfully. Compared with the reference algorithms, the fusion threshold in our scheme is fixed for different sequences. Experiments on typical sequences have successfully demonstrated the validity of the proposed scheme.