CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
3D shape-encoded particle filter for object tracking and its application to human body tracking
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Using Segmented 3D Point Clouds for Accurate Likelihood Approximation in Human Pose Tracking
International Journal of Computer Vision
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Particle filters (PF) are widely used in the Vision literature for visual object tracking. However, the selection and the tuning of the observation pdf (or likelihood function) involved in the particle weighting stage are often eclipsed. These considerations have a strong influence on the tracking performance, especially for human motion capture (HMC) due to the high number of degrees of freedom and the presence of local extrema in the state space. The proposed method is illustrated in the HMC context on a predefined set of likelihoods and assessed w.r.t. a ground truth provided by a commercial HMC system. This paper highlights the influence of their associated free parameters as well as their combination in order to characterize the optimal unified likelihood function. These insights lead to some heuristics to tackle the difficult problem of the likelihood function tuning.