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.
In REF , Sungphil et al., proposed a new method for human skeleton motion tracking using multiple Kinect V1 sensors.
Multiple Kinect Sensor Fusion for Human Skeleton Tracking Using Kalman Filtering
International Journal of Advanced Robotic Systems
Computer Science