Robust regression computation computation using iteratively reweighted least squares
SIAM Journal on Matrix Analysis and Applications
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Fast nonparametric belief propagation for real-time stereo articulated body tracking
Computer Vision and Image Understanding
Likelihood tuning for particle filter in visual tracking
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Nonparametric belief propagation
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Bayesian 3d human body pose tracking from depth image sequences
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
A data-driven approach for real-time full body pose reconstruction from a depth camera
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Efficient regression of general-activity human poses from depth images
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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The observation likelihood approximation is a central problem in stochastic human pose tracking. In this article we present a new approach to quantify the correspondence between hypothetical and observed human poses in depth images. Our approach is based on segmented point clouds, enabling accurate approximations even under conditions of self-occlusion and in the absence of color or texture cues. The segmentation step extracts small regions of high saliency such as hands or arms and ensures that the information contained in these regions is not marginalized by larger, less salient regions such as the chest. To enable the rapid, parallel evaluation of many poses, a fast ellipsoid body model is used which handles occlusion and intersection detection in an integrated manner. The proposed approximation function is evaluated on both synthetic and real camera data. In addition, we compare our approximation function against the corresponding function used by a state-of-the-art pose tracker. The approach is suitable for parallelization on GPUs or multicore CPUs.