Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering. (7th April 2016)

• Record Type: Journal Article
• Authors: Moon, Sungphill; Park, Youngbin; Ko, Dong Wook; Suh, Il Hong
• Is Part Of: International journal of advanced robotic systems. Volume 13:Number 2(2016)
• Publication Date: 2016-04-07
• Subjects: Skeleton Tracking -- Multiple Kinects -- Data Fusion -- Kalman Filter; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.5772/62415
• Languages: English
• ISSNs: 1729-8806

Abstract

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.