CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Strike a Pose: Tracking People by Finding Stylized Poses
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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 survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quasi-Monte Carlo filtering in nonlinear dynamic systems
IEEE Transactions on Signal Processing - Part I
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Visual Human Motion Capture (HMC) is a motivating challenge in the Computer Vision community as it enables lots of applications. Many methods have been proposed among which Particle Filters (PF) meet a great success. In this paper, we propose a new algorithm, mixing advantages of the PARTITIONED scheme and quasi random methods. We use a trinocular visual system to propose a comparative study of this particle filter against four other classical ones with respect to a ground truth provided by a commercial HMC system.