A spatio-temporal extension to Isomap nonlinear dimension reduction
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Appearance Manifolds from Video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
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
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers
International Journal of Computer Vision
Coupled Visual and Kinematic Manifold Models for Tracking
International Journal of Computer Vision
Robust spectral 3D-bodypart segmentation along time
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
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There has been growing interest in developing nonlinear dimensionality reduction algorithms for vision applications. Although progress has been made in recent years, conventional nonlinear dimensionality reduction algorithms have been designed to deal with stationary, or independent and identically distributed data. In this paper, we present a novel method that learns nonlinear mapping from time series data to their intrinsic coordinates on the underlying manifold. Our work extends the recent advances in learning nonlinear manifolds within a global coordinate system to account for temporal correlation inherent in sequential data. We formulate the problem with a dynamic Bayesian network and propose an approximate algorithm to tackle the learning and inference problems. Numerous experiments demonstrate the proposed method is able to learn nonlinear manifolds from time series data, and as a result of exploiting the temporal correlation, achieve superior results.