Object tracking using multiple fragments

This paper presents a low-cost tracking algorithm based on multiple multiple fragments, increasing robustness with respect to partial occlusions. Given the initial template representing the desired target, each pixel is classified into a different cluster based on a Mixture of Gaussians (MOG) model, and a set of disjoint fragments is created. The mean vector and covariance matrix of each fragment are computed, and the Mahalanobis distance is used to decide which pixels of the adjacent frame within a neighborhood are associated with each fragment. The template is then placed at the position that maximizes a similarity measure based on the number of matched points.