Shape-oriented segmentation with graph matching corroboration for silhouette tracking

This paper addresses the problem of advanced silhouette tracking with no prior information, and proposes shape-oriented segmentation together with graph matching corroboration. In terms of unified energy minimization, the shape-oriented graph cut in segmentation exploits the shape information by penalizing the feature points in alignment with shape-oriented map of adjacent frames. While reducing the temporal inconsistencies and improve the accuracy of segmentation, the energy model of graph matching is further designed to compensate the validity of segmentation. To be concrete, it is involved with structural matching cost and unmatched penalty cost to deal with occlusion during tracking. The effectiveness of the proposed scheme is shown with experiments on challenging real-world image sequences.