CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
2D Articulated Tracking with Dynamic Bayesian Networks
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
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3D upper body tracking and modeling is a topic greatly studied by the computer vision society because it is useful in a great number of applications such as human machine interface, companion robots animation or human activity analysis. However there is a challenging problem: the complexity of usual tracking algorithms, that exponentially increases with the dimension of the state vector, becomes too difficult to handle. To tackle this problem, we propose an approach that combines several annealing particle filters defined independently for each limb and belief propagation method to add geometrical constraints between individual filters. Experimental results on a real human gestures sequence will show that this combined approach leads to reliable results.