Human Motion Capture Using 3D Reconstruction Based on Multiple Depth Data

• Published in: 2013 IEEE International Conference on Systems, Man, and Cybernetics pp. 870 - 875
• Main Authors: Filali, Wassim, Masse, Jean-Thomas, Lerasle, Frederic, Boizard, Jean-Louis, Devy, Michel
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-10-2013
• Subjects: Conferences; Cybernetics; depth sensing; human posture reconstruction; machine learning; sensor fusion; voxel
• ISSN: 1062-922X; 2577-1655
• DOI: 10.1109/SMC.2013.153

Human motion is a critical aspect of interacting, even between people. It has become an interesting field to exploit in human-robot interaction. Even with today's computing power, it remains a difficult task to successfully follow the human's motion from image processing alone. New sensors were introduced, bringing depth sensing at low or no cost. Using this new technology, this paper presents a new methodology to see space with multiple depth sensors, using machine-learning technique, and features in voxel space to learn to reconstruct humans' joints in single, fused acquisitions. We back up and validate the procedure with ground truth acquired from commercial Motion Capture, and prove the approach to perform particularly well on an expansive set of motion and poses, and compare with current standard software on single depth sensors.