RFPose-OT: RF-based 3D human pose estimation via optimal transport theory
Cong YU1(), Dongheng ZHANG2, Zhi WU2, Zhi LU2, Chunyang XIE1, Yang HU3, Yan CHEN2()
1. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 2. School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China 3. School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China
This paper introduces a novel framework, i.e., RFPose-OT, to enable three-dimensional (3D) human pose estimation from radio frequency (RF) signals. Different from existing methods that predict human poses from RF signals at the signal level directly, we consider the structure difference between the RF signals and the human poses, propose a transformation of the RF signals to the pose domain at the feature level based on the optimal transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in a basic indoor environment, an occlusion indoor environment, and an outdoor environment demonstrate that RFPose-OT can predict 3D human poses with higher precision than state-of-the-art methods.
