Motion Capture With Ellipsoidal Skeleton Using Multiple Depth Cameras

Abstract

This paper introduces a novel motion capturing framework which works by minimizing the fitting error between an ellipsoid based skeleton and the input point cloud data captured by multiple depth cameras. The novelty of this method comes from that it uses the ellipsoids equipped with the spherical harmonics encoded displacement and normal functions to capture the geometry details of the tracked object. This method is also integrated with a mechanism to avoid collisions of bones during the motion capturing process. The method is implemented parallelly with CUDA on GPU and has a fast running speed without dedicated code optimization. The errors of the proposed method on the data from Berkeley Multimodal Human Action Database (MHAD) are within a reasonable range compared with the ground truth results. Our experiment shows that this method succeeds on many challenging motions which are failed to be reported by Microsoft Kinect SDK and not tested by existing works. In the comparison with the state-of-art marker-less depth camera based motion tracking work our method shows advantages in both robustness and input data modality.