Foreground Segmentation via Segments Tracking

In this paper we propose a video segmentation algorithm that in the final delineation of the object employs the graph-cut. A partitioning of the image based on pairwise region comparison is done at the beginning of each frame. A set of keypoints is tracked over time via optical flow to extract regions, which are likely to be parts of the object of interest. The tracked keypoints contribute towards better temporal coherence of the object segmentation. A probabilistic occupancy map of the object is extracted using such initial object segmentation and a probabilistic shape model. The map is utilized in a classifier that operates both on pixels and regions. The aim of the classifier is to extract a trimap consisting of foreground, background and unknown areas. The trimap is employed by graph-cut. The outcome of the graph-cut is used in on-line learning of the shape model. The performance of the algorithm is demonstrated on freely available test sequences.