CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Nonparametric density estimation for human pose tracking
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PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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Computer Vision and Image Understanding
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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.