Efficient silhouette based contour tracking

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. Object is represented by its silhouette as a candidate model in the candidate frame. A fuzzy k-nearest-neighbor (fuzzy k-NN) classifier is used to distinguish the object from the background. Instead of considering the whole training set, a subset of it is considered to classify each unlabeled sample in the target frame. A heuristic is suggested to generate the training subset from the corresponding neighborhood (of the candidate frame) of each unlabeled sample in the target frame, 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 two regions: object and background. The object region represents silhouette of the object and all others represent non-object region. Transition pixels from the non-object region to the object silhouette or the object silhouette to the non-object region are treated as the boundary or contour pixels of the object. Connecting the boundary pixels, contour or boundary of the object is extracted in the target frame. Hence, the object is tracked with its contour or boundary in the target candidate frame. We show a realization of the proposed method and demonstrate it on two benchmark video sequences. The effectiveness of the proposed method is established by comparing it with two state of the art contour tracking techniques, both qualitatively and quantitatively.