CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Tracking and modeling people in video sequences
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Human Body Model Acquisition and Tracking Using Voxel Data
International Journal of Computer Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Visual Hand Tracking Using Nonparametric Belief Propagation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 12 - Volume 12
Analyzing and Capturing Articulated Hand Motion in Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
3D tracking for gait characterization and recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
FABRIK: A fast, iterative solver for the Inverse Kinematics problem
Graphical Models
Overall design and implementation of the virtual glove
Computers in Biology and Medicine
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Optical motion capture can be classified as an inference problem: given the data produced by a set of cameras, the aim is to extract the hidden state, which in this case encodes the posture of the subject's body. Problems with motion capture arise due to the multi-modal nature of the likelihood distribution, the extremely large dimensionality of its state-space, and the narrow region of support of local modes. There are also problems with the size of the data and the difficulty with which useful visual cues can be extracted from it, as well as how informative these cues might be. Several algorithms exist that use stochastic methods to extract the hidden state, but although highly parallelisable in theory, such methods produce a heavy computational overhead even with the power of today's computers. In this paper we assume a set of pre-calibrated cameras and only extract the subject's silhouette as a visual cue. In order to describe the 2D silhouette data we define a 2D model consisting of conic fields. The resulting likelihood distribution is differentiable w.r.t. the state, meaning that its global maximum can be located fast using gradient ascent search, given manual initialisation at the first frame. In this paper we explain the construction of the model for tracking a human hand; we describe the formulation of the derivatives needed, and present initial results on both real and simulated data.