An algorithm for centroid-based tracking of moving objects

This article addresses the problem of tracking moving objects using deformable models. A Kalman-based algorithm a's presented, inspired on a new class of constrained clustering methods, recently proposed by Abrantes and Marques in the context of static shape estimation. A set of data centroids is tracked using intra-frame and inter-frame recursions. Centroids are computed as weighted sums of the edge points belonging to the object boundary. The use of centroids introduces competitive learning mechanisms in the tracking algorithm leading to improved robustness with respect to occlusion and contour sliding. Experimental results with traffic sequences are provided.