Real time Tracking by Detection of Human Motion in RGB D Camera Networks
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.
2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)