Robust weighted coarse-to-fine sparse tracking

Particle filter and sparse representation have been successfully applied to visual tracking in computer vision community. This paper proposes an adaptive weighted coarse-to-fine sparse tracking(WCFT) method based on particle filter framework. In this method, two series of templates, coarse templates and fine templates, are used to represent two different stages of human vision perception process respectively. Besides, the regularization parameter(weight) of each template is adapted according to its significance in representing the target. We also prove that our problem can be solved using an accelerated proximal gradient(APG) method. Moreover, we prove that the outstanding L1 tracker is a special case of our model and our method is more effective and efficient in general. The superiority of our system over current state-of-art tracking methods is demonstrated by a set of comprehensive experiments on public data sets.