Coupled prediction classification for robust visual tracking

This paper addresses the problem of robust template tracking in image sequences. Our work falls within the discriminative framework in which the observations at each frame yield direct probabilistic predictions of the state of the target. Our primary contribution is that we explicitly address the problem that the prediction accuracy for different observations varies, and in some cases, can be very low. To this end, we couple the predictor to a probabilistic classifier which, when trained, can determine the probability that a new observation can accurately predict the state of the target (that is, determine the "relevance" or "reliability" of the observation in question). In the particle filtering framework, we derive a recursive scheme for maintaining an approximation of the posterior probability of the state in which multiple observations can be used and their predictions moderated by their corresponding relevance. In this way, the predictions of the "relevant" observations are emphasized, while the predictions of the "irrelevant" observations are suppressed. We apply the algorithm to the problem of 2D template tracking and demonstrate that the proposed scheme outperforms classical methods for discriminative tracking both in the case of motions which are large in magnitude and also for partial occlusions.