Real time visual tracking using a spatially weighted von Mises mixture model

Abstract

A real time, kernel based, model-free tracking algorithm is proposed, which employs a weighted von Mises mixture as the target’s appearance model. The mixture weights, which are provided by a spatial kernel, along with the hue values are used in order to estimate the parameters of the weighted von Mises mixture model. The von Mises distribution is suitable for circular data and it is employed in order to eliminate drawbacks in kernel-based tracking caused by eventual shifts of the target’s histogram bins. The weights allow a mean shift-like gradient based optimization by maximizing the weighted likelihood, which would not be feasible in the context of a standard von Mises mixture. Moreover, as only the hue component of the target is involved, many quantities of the algorithm may be pre-calculated for given parameters and therefore the algorithm can perform in real time, which is experimentally confirmed. Finally, it is shown that the proposed method has comparative performance in terms of accuracy and robustness with other state-of-the-art tracking algorithms.