Improved skeleton estimation by means of depth data fusion from multiple depth cameras

In this work, we address the problem of human skeleton estimation when multiple depth cameras are available. We propose a system that takes advantage of the knowledge of the camera poses to create a collaborative virtual depth image of the person in the scene which consists of points from all the cameras and that represents the person in a frontal pose. This depth image is fed as input to the open-source body part detector in the Point Cloud Library. A further contribution of this work is the improvement of this detector obtained by introducing two new components: as a pre-processing, a people detector is applied to remove the background from the depth map before estimating the skeleton, while an alpha-beta tracking is added as a post-processing step for filtering the obtained joint positions over time. The overall system has been proven to effectively improve the skeleton estimation on two sequences of people in different poses acquired from two first-generation Microsoft Kinect. © Springer International Publishing AG 2017.