Learning Dynamical Models Using Expectation-Maximisation

Authors:
Ben North;Andrew Blake
Affiliations:
-;-
Venue:
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Year:
1998

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Abstract

Tracking with deformable contours in a filtering frame-work requir esa 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.