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.
Highlights
Capturing human motion can provide useful information for various robotic applications
We developed a human skeleton tracking system in which Kalman filtering employing a weighted measurement fusion method was used to combine different tracking results acquired from multiple Kinect sensors
Experiments were conducted to compare the precision of the proposed algorithm with that of a single Kinect sensor and simple averaging of five skeleton poses
Summary
Capturing human motion can provide useful information for various robotic applications. Transferring human skills and teleoperation of human motions in robots require 3D body/hand poses [22, 25]. There are two ways to capture human motion: marker-based motion capture, and markerless motion capture. One drawback of marker-based motion capture is that the performing subject has to wear a suit covered with sensors or markers. It is not necessary for the subject to wear a suit in markerless motion capture, which has helped the technology gain significant attention recently. Markerless motion capture is challeng‐ ing in the absence of accurate depth information. The introduction of Kinect had a significant impact in robotics as well as full-body pose tracking
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