Learning Bayesian Tracking for Motion Estimation

TL;DR: This paper proposes a novel way of doing Bayesian tracking called channel- based tracking, related to grid-based tracking methods, but diers 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.

Abstract: 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 diers 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 fil- ters, using a standard dataset from the literature. A simple computer vision experiment is shown to illustrate possible applications.