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Abstract

In this paper we present an approach to use prior knowledge in the particle
filter framework for 3D tracking, i.e. estimating the state parameters such as
joint angles of a 3D object. The probability of the object’s states, including
correlations between the state parameters, is learned a priori from training
samples. We introduce a framework that integrates this knowledge into the
family of particle filters and particularly into the annealed particle filter
scheme. Furthermore, we show that the annealed particle filter also works with
a variational model for level set based image segmentation that does not rely
on background subtraction and, hence, does not depend on a static background.
In our experiments, we use a four camera set-up for tracking the lower part of
a human body by a kinematic model with 18 degrees of freedom. We demonstrate
the increased accuracy due to the prior knowledge and the robustness of our
approach to image distortions. Finally, we compare the results of our
multi-view tracking system quantitatively to the outcome of an industrial
marker based tracking system.