In this paper, we introduce a novel framework for automatically evaluating the quality of 3D tracking results obtained from markerless motion capturing. In our approach, we use additional inertial sensors to generate suitable reference information. In contrast to previously used marker-based evaluation schemes, inertial sensors are inexpensive, easy to operate, and impose comparatively weak additional constraints on the overall recording setup with regard to location, recording volume, and illumination. On the downside, acceleration and rate of turn data as obtained from such inertial systems turn out to be unsuitable representations for tracking evaluation. As our main contribution, we show how tracking results can be analyzed and evaluated on the basis of suitable limborientations, which can be derived from 3D tracking results as well as from enhanced inertial sensors xed on these limbs. Our experiments on various motion sequences of different complexity demonstrate that such limb orientations constitute a suitable mid-level representation for robustly detecting most of the tracking errors. In particular, our evaluation approach reveals also miscon gurations and twists of the limbs that can hardly be detected from traditional evaluation metrics.
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