Tracking with deformable contours in a filtering framework requires a dynamical model for prediction. For any given application, tracking is improved by having an accurate model, learned from training data. We develop a method for learning dynamical models from training sequences, explicitly taking account of the fact that training data are noisy measurements and not true states. By introducing an 'augmented-state smoothing filter' we show how the technique of Expectation-Maximisation can be applied to this problem, and show that the resulting algorithm produces more robust and accurate tracking.
Learning dynamical models using expectation-maximisation
1998-01-01
849259 byte
Conference paper
Electronic Resource
English
Learning Dynamical Models Using Expectation-Maximisation
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