In this contribution we introduce the Multiocular Con- tracting Curve Density algorithm (MOCCD), a novel method for fitting a 3D parametric curve. The MOCCD is integrated into a tracking system and its suitability for tracking human body parts in 3D in front of cluttered back- ground is examined. The developed system can be applied to a variety of body parts, as the object model is replaceable in a simple manner. Based on the example of tracking the human hand-forearm limb it is shown that the use of three MOCCD algorithms with three different kinematic models within the system leads to an accurate and temporally sta- ble tracking. All necessary information is obtained from the images, only a coarse initialisation of the model pa- rameters is required. The investigations are performed on 14 real-world test sequences. These contain movements of different hand-forearm configurations in front of a complex cluttered background. We find that the use of three cameras is essential for an accurate and temporally stable system performance since otherwise the pose estimation and track- ing results are strongly affected by the aperture problem. Our best method achieves 95% recognition rate, compared to about 30% for the reference methods of 3D Active Con- tours and a curve model tracked by a Particle Filter. Hence only 5% of the estimated model points exceed a distance of 12 cm with respect to the ground truth, using the proposed method.
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