Matthias Weber
ABSTRACT
Motion Capture is a powerful approach to track 3D position, usually utilizing markers. Especially passive markers do not hinder natural motion. Unfortunately, such systems do not provide any information about which anatomical landmark their markers belong to. Multiple manual actions are often required to guide the tracking process. This work presents a hybrid approach for nearly fully automatic identification and tracking of such markers. It encompasses three methods for identification, using PCA-based alignment or tree-based optimization, and tracking, using a neural network with self-organizing characteristics.
- Dorfmüller-Ulhaas, K. 2003. Robust Optical User Motion Tracking Using a Kalman Filter. Tech. Rep. 2003--6, University of Augsburg, Institut für Informatik.Google Scholar
- Hornung, A., and Sar-Dessai, S. 2005. Self-Calibrating Optical Motion Tracking for Articulated Bodies. In Proc. IEEE Conf. on Virtual Reality, IEEE Computer Society, 75--82. Google ScholarDigital Library
- O'Brien, J. F., Bodenheimer, R., Brostow, G., and Hodgins, J. K. 2000. Automatic Joint Parameter Estimation from Magnetic Motion Capture Data. In Graphics Interface, 53--60.Google Scholar
- Weber, M., Ben Amor, H., and Alexander, T. 2008. Enhancing Motion Capture Performance by Means of an Internal Anthropometric Skeleton Model. In SAE Digital Human Modeling Conference.Google Scholar
Index Terms
- A hybrid approach towards fully automatic 3D marker tracking
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