  This paper presents a novel real-time tracking system capable of improving
body pose estimation algorithms in distributed camera networks. The first stage
of our approach introduces a linear Kalman filter operating at the body joints
level, used to fuse single-view body poses coming from different detection
nodes of the network and to ensure temporal consistency between them. The
second stage, instead, refines the Kalman filter estimates by fitting a
hierarchical model of the human body having constrained link sizes in order to
ensure the physical consistency of the tracking. The effectiveness of the
proposed approach is demonstrated through a broad experimental validation,
performed on a set of sequences whose ground truth references are generated by
a commercial marker-based motion capture system. The obtained results show how
the proposed system outperforms the considered state-of-the-art approaches,
granting accurate and reliable estimates. Moreover, the developed methodology
constrains 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 importance
for a large number of real-world applications.
