Motion capture systems today have to deliver high quality motion data, while being flexible and easily adaptable to different actors. Therefore, accurately determining parameters of a subject's skeletal structure is crucial. Inferring these values automatically from optical motion capture data without additional measurements, however, is a challenging task. This thesis describes the steps necessary to calculate the joint positions and limb lengths using data from a passive optical tracking system. The algorithm is a multi-stage process that includes the tasks of automatic marker labeling, limb-wise clustering of markers and calculation of joint positions. Finally an estimate of the topology and the parameters of the articulated structure are computed. Since the topology is inferred from the data, no model has to exist in advance. This in turn makes the implemented system flexible enough to capture not only human motions, but motions of an arbitrary articulated structure, without any adaptations or additional effort. The core functionality of the system, which is the skeleton fitting task, is done using a distance function, that is applied to marker positions. This function then is minimized by a non-linear minimization algorithm. Tests of the system have been performed with artificially generated data, and a construction of rods linked with articulations. The results show high accuracy for the artificial data. For the tracked data sets also satisfactory outcome is produced.
C. Schönauer: "Skeletal Structure Generation for Optical Motion Capture"; Supervisor: H. Kaufmann; Institute for Software Technology and Interactive Systems, 2008.
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