Skeleton estimation and tracking by means of depth data fusion from depth camera networks.

In this work, we describe an approach for estimation and tracking of the skeleton of the human body from camera networks exploiting only depth data. The algorithm takes advantage of multiple views by building and merging together the 3D point clouds. The final skeleton is computed from a virtual depth image generated from this point cloud by means of back-projection to a reference camera image plane. Before the back-projection, the person point cloud is frontalized with respect to the reference camera, so that the virtual depth image represents the person from a frontal viewpoint and the accuracy of the skeleton estimation algorithm is maximized. Our experiments show how the proposed approach boosts the performance with respect to other state-of-the-art approaches. Moreover, the proposed algorithm requires low computational burden, thus running in real-time.