Three-dimensional Human Tracking of a Mobile Robot by Fusion of Tracking Results of Two Cameras
This work addresses the problem of efficient 3D human tracking for mobile robots, but it appears incremental as it builds on existing methods like OpenPose with a new stereo vision framework.
The paper tackles the problem of obtaining accurate 3D information for human tracking by integrating detection results from two cameras, proposing a new stereo vision framework to address issues like incorrect matching and high computational cost in calibration, and verifies its effectiveness through target-tracking experiments.
This paper proposes a process that uses two cameras to obtain three-dimensional (3D) information of a target object for human tracking. Results of human detection and tracking from two cameras are integrated to obtain the 3D information. OpenPose is used for human detection. In the case of a general processing a stereo camera, a range image of the entire scene is acquired as precisely as possible, and then the range image is processed. However, there are problems such as incorrect matching and computational cost for the calibration process. A new stereo vision framework is proposed to cope with the problems. The effectiveness of the proposed framework and the method is verified through target-tracking experiments.