Template Trackingwith Observation Relevance Determination

Abstract

This paper addresses the problem of template tracking in the presence of occlusions, clutter and rapid motion. We adopt a learning approach, using a Bayesian Mixture of Experts (BME), in which observations at each frame yield direct predictions of the state (e.g. position / scale) of the tracked target. In contrast to other methods in the literature, we explicitly address the problem that the prediction accuracy can deteriorate drastically for observations that are not similar to the ones in the training set; such observations are common in case of partial occlusions or of fast motion. To do so, we couple the BME with a probabilistic kernel-based classifier which, when trained, can determine the probability that a new/unseen observation can accurately predict the state of the target (the 'relevance' of the observation in question). In addition, in the particle filtering framework, we derive a recursive scheme for maintaining an approximation of the posterior probability of the target's state in which the probabilistic predictions of multiple observations are moderated by their corresponding relevance. We apply the algorithm in the problem of 2D template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking in case of motions large in magnitude and of partial occlusions.