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” observat...

• Type: Journal
• Year: 2010
• Where: PAMI
• Authors: Ioannis Patras, Edwin R. Hancock
• Keywords: Direct Probabilistic Predictions, Observations, PAMI 2010, Predictions