3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Pictorial Structures for Object Recognition
International Journal of Computer Vision
Visual Hand Tracking Using Nonparametric Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
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
Learning to Estimate Human Pose with Data Driven Belief Propagation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Measure Locally, Reason Globally: Occlusion-sensitive Articulated Pose Estimation
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Human body pose detection using Bayesian spatio-temporal templates
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
Loopy belief propagation for approximate inference: an empirical study
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Constructing free-energy approximations and generalized belief propagation algorithms
IEEE Transactions on Information Theory
GPU-accelerated tracking of the motion of 3D articulated figure
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
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In this paper we propose a self-initialized method for human pose estimation from multiple cameras. A graphical model for the articulated body is defined through explicit kinematic and structural constraints, which allows for any plausible body configuration and avoids learning the joint distributions from training data. Nonparametric belief propagation (NBP) is used to infer the marginal distributions. However, to address the problem of the inference being trapped in local optima and to achieve fast convergence, a reasonably good pose initialization is required. A bottom-up approach is used to detect body parts distributedly in local processing of each camera. 3D Geometry correspondence relates 2D camera observations spatially to generate a rough pose estimation to initialize node marginal distribution. The marginal distributions are then refined through NBP. Estimated 3D body joint positions are quantitatively analyzed with motion capture data.