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In this paper a new approach to the reconstruction of 3D trajectories of dense marker sets is proposed. The key element is the use of multiple passes to reconstruct the spatiotemporal structure of the movement with high reliability. First the tracking procedure computes a coarse structure of the motion, which is then recursively refined disambiguating difficult classification of the markers. The tracking procedure is based on integrating the temporal dimension in the matching process, by analyzing strings instead of points to derive more robust matches. Strings are analyzed using smoothness, n-focal constraints, and fitting of a skeleton to derive a proper matching. An innovative augmented reality-like interface greatly simplifies the labeling task. Lastly, a proper value for the critical parameters is automatically derived. Results on real data show that the system is able to produce a robust and largely complete set of trajectories, which greatly minimize the time required by post-processing.