Open AccessMotion Capture of the Human Body Using Multiple Depth Sensors
Author(s) -
Kim Yejin,
Baek Seongmin,
Bae ByungChull
Publication year - 2017
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.17.2816.0045
Subject(s) - motion capture , computer vision , point cloud , artificial intelligence , computer science , process (computing) , coordinate system , motion (physics) , tracking (education) , match moving , point (geometry) , rotation (mathematics) , joint (building) , engineering , mathematics , geometry , psychology , architectural engineering , pedagogy , operating system
The movements of the human body are difficult to capture owing to the complexity of the three‐dimensional skeleton model and occlusion problems. In this paper, we propose a motion capture system that tracks dynamic human motions in real time. Without using external markers, the proposed system adopts multiple depth sensors (Microsoft Kinect) to overcome the occlusion and body rotation problems. To combine the joint data retrieved from the multiple sensors, our calibration process samples a point cloud from depth images and unifies the coordinate systems in point clouds into a single coordinate system via the iterative closest point method. Using noisy skeletal data from sensors, a posture reconstruction method is introduced to estimate the optimal joint positions for consistent motion generation. Based on the high tracking accuracy of the proposed system, we demonstrate that our system is applicable to various motion‐based training programs in dance and Taekwondo.