Learning Bayesian Tracking for Motion Estimation

A common computer vision problem is to track a physical object through an image sequence. In general, the observations that are made in a single image determine the actual state only partially and information from several views has to be merged. A principled and well-established way of fusing information is the Bayesian framework. In this paper, we propose a novel way of doing Bayesian tracking called channel-based tracking. The method is related to grid-based tracking methods, but differs in two aspects: The applied sampling functions, i.e., the bins, are smooth and overlapping and the system and measurement models are learned from a training set. The results from the channel-based tracker are compared to state-of-the-art tracking methods based on particle filters, using a standard dataset from the literature. A simple computer vision experiment is shown to illustrate possible applications.

Full video sequence available at http://www.cvl.isy.liu.se/~mfe/tracking.avi