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Object shape delineation during the tracking process plays important roles in correctly interpreting tracked results, providing visually meaningful outcomes, and furthermore assisting better motion estimation. For the majority of object tracking scenario, the emphasis has been put on achieving robust motion estimation in different situations; and object shape delineation, though critical, has not been paid enough attention due to its ill-posed nature. Approaches have been proposed by assuming the similarity of object pixels in the vicinities of the boundaries between the current frame and the previous one. Such an assumption is usually broken down when occlusion occurs; instead, our implementation is based on a stronger assumption: the local properties of object silhouette should be similar to those of the nearby object pixels. In this paper, we are going to address how to depict object boundary by a novel double-region growing and statistical pattern classification approach. Different from using a single point as a seed as which is a typical way for region growing, our seeds are segmented contours; also instead of growing outward in a single direction from the seed, we propose a two-directional region growing approach. Finally the best object boundary candidates are arbitrated from the dual-region growing results by a statistical classification approach.