Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
A Non-Iterative Greedy Algorithm for Multi-frame Point Correspondence
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Self-Calibrating Optical Motion Tracking for Articulated Bodies
VR '05 Proceedings of the 2005 IEEE Conference 2005 on Virtual Reality
Automatic Kinematic Chain Building from Feature Trajectories of Articulated Objects
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
A dynamic Bayesian approach to computational Laban shape quality analysis
Advances in Human-Computer Interaction
Human-computer intelligent interaction: a survey
HCI'07 Proceedings of the 2007 IEEE international conference on Human-computer interaction
Reconstructing 3D tree models using motion capture and particle flow
International Journal of Computer Games Technology
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We present a completely autonomous algorithm for the real-time creation of a moving subject's kinematic model from optical motion capture data and with no a priori information. Our approach solves marker tracking, the building of the kinematic model, and the tracking of the body simultaneously. The novelty lies in doing so through a unifying Markov random field framework, which allows the kinematic model to be built incrementally and in real-time. We validate the potential of this method through experiments in which the system is able to accurately track the movement of the human body without an a priori model, as well as through experiments on synthetic data.