A real time anatomical converter for human motion capture
Proceedings of the Eurographics workshop on Computer animation and simulation '96
Understanding Motion Capture for Computer Animation and Video Games
Understanding Motion Capture for Computer Animation and Video Games
Interpolation Synthesis of Articulated Figure Motion
IEEE Computer Graphics and Applications
Verbs and Adverbs: Multidimensional Motion Interpolation
IEEE Computer Graphics and Applications
Motion Tracking: No Silver Bullet, but a Respectable Arsenal
IEEE Computer Graphics and Applications
Skeleton-Based Motion Capture for Robust Reconstruction of Human Motion
CA '00 Proceedings of the Computer Animation
Self-Calibrating Optical Motion Tracking for Articulated Bodies
VR '05 Proceedings of the 2005 IEEE Conference 2005 on Virtual Reality
Optical Tracking and Automatic Model Estimation of Composite Interaction Devices
VR '06 Proceedings of the IEEE conference on Virtual Reality
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Occlusion-Resistant Camera Design for Acquiring Active Environments
IEEE Computer Graphics and Applications
Logistic tensor regression for classification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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A common problem in optical motion capture of human-body movement is the so-called missing marker problem. The occlusion of markers can lead to significant problems in tracking accuracy unless a continuous flow of data is guaranteed by interpolation or extrapolation algorithms. Since interpolation algorithms require data sampled before and after an occlusion, they cannot be used for real-time applications. Extrapolation algorithms only require data sampled before an occlusion. Other algorithms require statistical data and are designed for post-processing. In order to bridge sampling gaps caused by occluded markers and hence to improve 3D real-time motion capture, we suggest a computationally cost-efficient extrapolation algorithm partly combined with a so-called constraint matrix. The realization of this prediction algorithm does not require statistical data nor does it rely on an underlying kinematic human model with pre-defined marker distances. Under the assumption that human motion can be linear, circular, or a linear combination of both, a prediction method is realized. The paper presents measurements of a circular movement wherein a marker is briefly lost. The suggested extrapolation method behaves well for a reasonable number of frames, not exceeding around two seconds of time.