Model-Based Analysis of Hand Posture
IEEE Computer Graphics and Applications
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Recovering 3D Human Pose from Monocular Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
International Journal of Computer Vision
Regression-based Hand Pose Estimation from Multiple Cameras
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Gaussian Process Dynamical Models for Human Motion
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
Multivariate relevance vector machines for tracking
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
3D articulated hand tracking based on behavioral model
Transactions on Edutainment VIII
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In this paper, we propose a novel dimensionality reduction method, temporal neighbor preserving embedding (TNPE), to learn the low-dimensional intrinsic motion manifold of articulated objects. The method simultaneously learns the embedding manifold and the mapping from an image feature space to an embedding space by preserving the local temporal relationship hidden in sequential data points. Then tracking is formulated as the problem of estimating the configuration of an articulated object from the learned central embedding representation. To solve this problem, we combine Bayesian mixture of experts (BME) with Gaussian mixture model (GMM) to establish a probabilistic non-linear mapping from the embedding space to the configuration space. The experimental result on articulated hand and human pose tracking shows an encouraging performance on stability and accuracy.