Switching Observation Models for Contour Tracking in Clutter

We propose a generative model approach to contour tracking against non-stationary clutter and to coping with occlusions by explicit modelling and inferring. The proposed dynamic Bayesian networks consist of multiple hidden processes which model the target, the clutter and the occlusions. The image observation models, which depict the generation of the image features, are conditioned on all the hidden processes. Based on this framework, the tracker can automatically switch among different observation models according to the hidden states of the clutter and occlusions. In addition, the inference of these hidden states provides self-evaluations for the tracker. The tracking and inferencing are implemented based on sequence Monte Carlo techniques. The effectiveness of the proposed approach to robust tracking and inferring non-stationary clutter and occlusion is demonstrated for a variety of image sequences.