IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithm 548: Solution of the Assignment Problem [H]
ACM Transactions on Mathematical Software (TOMS)
Understanding Motion Capture for Computer Animation and Video Games
Understanding Motion Capture for Computer Animation and Video Games
Monocuolar Perception of Biological Motion - Clutter and Partial Occlusion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Joint Probabilistic Techniques for Tracking Multi-Part Objects
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Model-based tracking of self-occluding articulated objects
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Monocular Perception of Biological Motion Detection and Labeling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
Operations Research: An Introduction (8th Edition)
Operations Research: An Introduction (8th Edition)
Computer aided orthodontics measures evaluation
MACMESE'11 Proceedings of the 13th WSEAS international conference on Mathematical and computational methods in science and engineering
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We present a technique for performing the tracking stage of optical motion capture which retains, at each time frame, multiple marker association hypotheses and estimates of the subject's position. Central to this technique are the equations for calculating the likelihood of a sequence of association hypotheses, which we develop using a Bayesian approach. The system is able to perform motion capture using fewer cameras and a lower frame rate than has been used previously, and does not require the assistance of a human operator. We conclude by demonstrating the tracker on real data and provide an example in which our technique is able to correctly determine all marker associations and standard tracking techniques fail.