Efficient silhouette-based contour tracking using local information

In this article, we present an algorithm that can efficiently track the contour extracted from silhouette of the moving object of a given video sequence using local neighborhood information and fuzzy k-nearest-neighbor classifier. To classify each unlabeled sample in the target frame, instead of considering the whole training set, a subset of it is considered depending on the amount of motion of the object between immediate previous two consecutive frames. This technique makes the classification process faster and may increase the classification accuracy. Classification of the unlabeled samples in the target frame provides object (silhouette of the object) and background (non-object) regions. Transition pixels from the non-object region to the object silhouette and vice versa are treated as the boundary or contour pixels of the object. Contour or boundary of the object is extracted by connecting the boundary pixels and the object is tracked with this contour in the target frame. We show a realization of the proposed method and demonstrate it on eight benchmark video sequences. The effectiveness of the proposed method is established by comparing it with six state of the art contour tracking techniques, both qualitatively and quantitatively.