Probabilistic visual tracking via robust template matching and incremental subspace update

In this paper, we present a probabilistic algorithm for visual tracking that incorporates robust template matching and incremental subspace update. There are two template matching methods used in the tracker: one is robust to small perturbation and the other to background clutter. Each method yields a probability of matching. Further, the templates are modeled using mixed probabilities and updated once the templates in the library cannot capture the variation of object appearance. We also model the tracking history using a nonlinear subspace that is described by probabilistic kernel principal components analysis, which provides a third probability. The most-recent tracking result is added to the nonlinear subspace incrementally. This update is performed efficiently by augmenting the kernel Gram matrix with one row and one column. The product of the three probabilities is defined as the observation likelihood used in a particle filter to derive the tracking result. Experimental results demonstrate the efficiency and effectiveness of the proposed algorithm.