Simulation of Face Pose Tracking System using Adaptive Vision Switching
Hyuno Kim, Ryo Ito, Seohyun Lee, Yuji Yamakawa, Masatoshi Ishikawa · 2019
In this paper, a face pose tracking method using adaptive vision switching with a networked camera system is proposed and verified through simulation with a facial computer graphics model. The proposed method improves the pose estimation accuracy of the conventional techniques that use monocular camera. Additionally, adaptive vision switching provides a new pose tracking experience, pose lock-on, which has the potential for object recognition tasks including face recognition under dynamic conditions. Quantitative analysis and tracking demonstration with simulations of the proposed system are conducted and described.