Abstract
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.
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Piazza, T., Lundström, J., Kunz, A., Fjeld, M. (2009). Predicting Missing Markers in Real-Time Optical Motion Capture. In: Magnenat-Thalmann, N. (eds) Modelling the Physiological Human. 3DPH 2009. Lecture Notes in Computer Science, vol 5903. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10470-1_11
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DOI: https://doi.org/10.1007/978-3-642-10470-1_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10468-8
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