Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities
暂无分享,去创建一个
[1] Carl E. Rasmussen,et al. A Unifying View of Sparse Approximate Gaussian Process Regression , 2005, J. Mach. Learn. Res..
[2] Trevor Darrell,et al. Sparse probabilistic regression for activity-independent human pose inference , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[3] Jixu Chen,et al. Switching Gaussian Process Dynamic Models for simultaneous composite motion tracking and recognition , 2009, CVPR.
[4] J. Tenenbaum,et al. A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.
[5] Luc Van Gool,et al. Learning Generative Models for Multi-Activity Body Pose Estimation , 2008, International Journal of Computer Vision.
[6] Neil D. Lawrence,et al. Non-linear matrix factorization with Gaussian processes , 2009, ICML '09.
[7] Ian D. Reid,et al. Articulated Body Motion Capture by Stochastic Search , 2005, International Journal of Computer Vision.
[8] Neil D. Lawrence,et al. Learning for Larger Datasets with the Gaussian Process Latent Variable Model , 2007, AISTATS.
[9] Neil A. Thacker,et al. Real-time Body Tracking Using a Gaussian Process Latent Variable Model , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[10] Rui Li,et al. 3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers , 2009, International Journal of Computer Vision.
[11] Luc Van Gool,et al. 2D Action Recognition Serves 3D Human Pose Estimation , 2010, ECCV.
[12] David J. Fleet,et al. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE Gaussian Process Dynamical Model , 2007 .
[13] Trevor Darrell,et al. Rank priors for continuous non-linear dimensionality reduction , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.
[14] David J. Fleet,et al. Stochastic Tracking of 3D Human Figures Using 2D Image Motion , 2000, ECCV.
[15] Neil D. Lawrence,et al. Probabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models , 2005, J. Mach. Learn. Res..
[16] David J. Fleet,et al. 3D People Tracking with Gaussian Process Dynamical Models , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[17] David J. Fleet,et al. Dynamical binary latent variable models for 3D human pose tracking , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[18] Narendra Ahuja,et al. Learning Nonlinear Manifolds from Time Series , 2006, ECCV.
[19] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[20] Baoxin Li,et al. Learning Motion Correlation for Tracking Articulated Human Body with a Rao-Blackwellised Particle Filter , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[21] Rui Li,et al. Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[22] Vladimir Pavlovic,et al. Learning Switching Linear Models of Human Motion , 2000, NIPS.
[23] David J. Fleet,et al. Lattice Particle Filters , 2001, UAI.
[24] David J. Fleet,et al. Priors for people tracking from small training sets , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[25] Geoffrey E. Hinton,et al. Global Coordination of Local Linear Models , 2001, NIPS.
[26] Michael J. Black,et al. HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.
[27] Hans-Peter Seidel,et al. Stabilizing motion tracking using retrieved motion priors , 2009, 2009 IEEE 12th International Conference on Computer Vision.