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This paper presents a novel real-time tracking system capable of improvingbody pose estimation algorithms in distributed camera networks. The first stageof our approach introduces a linear Kalman filter operating at the body jointslevel, used to fuse single-view body poses coming from different detectionnodes of the network and to ensure temporal consistency between them. Thesecond stage, instead, refines the Kalman filter estimates by fitting ahierarchical model of the human body having constrained link sizes in order toensure the physical consistency of the tracking. The effectiveness of theproposed approach is demonstrated through a broad experimental validation,performed on a set of sequences whose ground truth references are generated bya commercial marker-based motion capture system. The obtained results show howthe proposed system outperforms the considered state-of-the-art approaches,granting accurate and reliable estimates. Moreover, the developed methodologyconstrains neither the number of persons to track, nor the number, position,synchronization, frame-rate, and manufacturer of the RGB-D cameras used.Finally, the real-time performances of the system are of paramount importancefor a large number of real-world applications.

Real-time Tracking-by-Detection of Human Motion in RGB-D Camera Networks