Visual Tracking by Weighted Likelihood Maximization

Vasileios Karavasilis, Christophoros Nikou, Aristidis Likas

Abstract: A probabilistic real time tracking algorithm is proposed. The distribution of the target is represented by a Gaussian mixture model (GMM) and the weighted likelihood of the target is maximized in order to localize it in an image sequence. The role of the weight is important as it allows gradient based optimization to be performed, which would not be feasible in a context of standard likelihood representations. The algorithm models both the object to be tracked and local background elements and handles scale changes in target's appearance. It is experimentally demonstrated that the algorithm runs in real time, and it is at least at the same performance level with the mean shift algorithm while it provides more accurate target localization in non trivial scenarios (e.g. shadows).