Neural Computation
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
GTM: the generative topographic mapping
Neural Computation
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Cardboard People: A Parameterized Model of Articulated Image Motion
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Filtering Using a Tree-Based Estimator
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Generative modeling for continuous non-linearly embedded visual inference
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces
ACM SIGGRAPH 2004 Papers
Priors for People Tracking from Small Training Sets
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
3D People Tracking with Gaussian Process Dynamical Models
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Quantitative Evaluation of Video-based 3D Person Tracking
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topologically-constrained latent variable models
Proceedings of the 25th international conference on Machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Learning nonlinear manifolds from time series
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Guest Editorial: State of the Art in Image- and Video-Based Human Pose and Motion Estimation
International Journal of Computer Vision
Smart video systems in police cars
Proceedings of the international conference on Multimedia
2D action recognition serves 3D human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Multi-view 3D Human Pose Estimation in Complex Environment
International Journal of Computer Vision
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
Full body performance capture under uncontrolled and varying illumination: a shading-based approach
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part IV
Computer Vision and Image Understanding
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A major challenge in applying Bayesian tracking methods for tracking 3D human body pose is the high dimensionality of the pose state space. It has been observed that the 3D human body pose parameters typically can be assumed to lie on a low-dimensional manifold embedded in the high-dimensional space. The goal of this work is to approximate the low-dimensional manifold so that a low-dimensional state vector can be obtained for efficient and effective Bayesian tracking. To achieve this goal, a globally coordinated mixture of factor analyzers is learned from motion capture data. Each factor analyzer in the mixture is a "locally linear dimensionality reducer" that approximates a part of the manifold. The global parametrization of the manifold is obtained by aligning these locally linear pieces in a global coordinate system. To enable automatic and optimal selection of the number of factor analyzers and the dimensionality of the manifold, a variational Bayesian formulation of the globally coordinated mixture of factor analyzers is proposed. The advantages of the proposed model are demonstrated in a multiple hypothesis tracker for tracking 3D human body pose. Quantitative comparisons on benchmark datasets show that the proposed method produces more accurate 3D pose estimates over time than those obtained from two previously proposed Bayesian tracking methods.