Abstract
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.
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© 2007 Springer-Verlag Berlin Heidelberg
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Rajko, S., Qian, G. (2007). Real-Time Automatic Kinematic Model Building for Optical Motion Capture Using a Markov Random Field. In: Lew, M., Sebe, N., Huang, T.S., Bakker, E.M. (eds) Human–Computer Interaction. HCI 2007. Lecture Notes in Computer Science, vol 4796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75773-3_8
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DOI: https://doi.org/10.1007/978-3-540-75773-3_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75772-6
Online ISBN: 978-3-540-75773-3
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