CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Statistical Optimization for Geometric Computation: Theory and Practice
Statistical Optimization for Geometric Computation: Theory and Practice
Information Retrieval
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Tracking People with Twists and Exponential Maps
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Statistical modeling for networked video: coding optimization, error concealment and traffic analysis
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
Exploiting Structural Hierarchy in Articulated Objects Towards Robust Motion Capture
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Gesture recognition with a Time-Of-Flight camera
International Journal of Intelligent Systems Technologies and Applications
Hand gesture recognition with a novel IR time-of-flight range camera: a pilot study
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
Multi-activity tracking in LLE body pose space
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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This paper presents a Time-of-Flight (ToF) camera based system for hand motion and gesture tracking. A 27 degree of freedom (DOF) hand model is constructed and fleshed out by ellipsoids. This allows the synthesis of range images of the model through projective geometry. The hand pose is then tracked with a particle filter by statistically measuring the hypothetical pose against the ToF input image; where the inside/outside alignment of the hand pixels and the depth differences serve as classifying metrics. The high DOF tracking problem for the particle filter is addressed by reducing the high dimensionality of the joint angle space to a low dimensional space via Principal Component Analysis (PCA). The basis vectors are learned from a few basic model configurations and the transformations between these poses. This results in a system capable of practical hand tracking in a restricted gesture configuration space.