期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2016
卷号：13
期号：2
页码：65
DOI：10.5772/62415
语种：English
出版社：SAGE Publications
摘要：Kinect sensors are able to achieve considerable skeleton tracking performance in a convenient and low-cost manner. However, Kinect sensors often generate poor skeleton poses due to self-occlusion, which is a common problem among most vision-based sensing systems. A simple way to solve this problem is to use multiple Kinect sensors in a workspace and combine the measurements from the different sensors. However, this method creates a new issue known as the data fusion problem. In this research, we developed a human skeleton tracking system using the Kalman filter framework, in which multiple Kinect sensors are used to correct inaccurate tracking data from a single Kinect sensor. Our contribution is to propose a method to determine the reliability of each tracked 3D position of a joint and then combine multiple observations based on measurement confidence. We evaluate the proposed approach by comparison with the ground truth obtained using a commercial marker-based motion-capture system.
关键词：Skeleton Tracking; Multiple Kinects; Data Fusion; Kalman Filter
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