CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2023-06-15
Cited: 0
Clicked: 1494
Citations: Bibtex RefMan EndNote GB/T7714
Cong YU, Dongheng ZHANG, Zhi WU, Zhi LU, Chunyang XIE, Yang HU, Yan CHEN. RFPose-OT: RF-based 3D human pose estimation via optimal transport theory[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(10): 1445-1457.
@article{title="RFPose-OT: RF-based 3D human pose estimation via optimal transport theory",
author="Cong YU, Dongheng ZHANG, Zhi WU, Zhi LU, Chunyang XIE, Yang HU, Yan CHEN",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="10",
pages="1445-1457",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200550"
}
%0 Journal Article
%T RFPose-OT: RF-based 3D human pose estimation via optimal transport theory
%A Cong YU
%A Dongheng ZHANG
%A Zhi WU
%A Zhi LU
%A Chunyang XIE
%A Yang HU
%A Yan CHEN
%J Frontiers of Information Technology & Electronic Engineering
%V 24
%N 10
%P 1445-1457
%@ 2095-9184
%D 2023
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2200550
TY - JOUR
T1 - RFPose-OT: RF-based 3D human pose estimation via optimal transport theory
A1 - Cong YU
A1 - Dongheng ZHANG
A1 - Zhi WU
A1 - Zhi LU
A1 - Chunyang XIE
A1 - Yang HU
A1 - Yan CHEN
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 24
IS - 10
SP - 1445
EP - 1457
%@ 2095-9184
Y1 - 2023
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2200550
Abstract: 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.
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