Through-Wall Human Pose Estimation Using Radio Signals
Name
Zhao_Through-Wall_Human_Pose_CVPR_2018_paper.pdf
Description
Published version
Size
1.47 MB
Format
Adobe PDF
Checksum (MD5)
f549bcb179669e0da9748bdcdad26ba0
Author(s)
Zhao, Mingmin
Li, Tianhong
Alsheikh, Mohammad Abu
Tian, Yonglong
Zhao, Hang
Torralba, Antonio
Katabi, Dina
Date Issued
June 2018
Publisher
IEEE
Citation
Zhao, Mingmin, Li, Tianhong, Alsheikh, Mohammad Abu, Tian, Yonglong, Zhao, Hang et al. 2018. "Through-Wall Human Pose Estimation Using Radio Signals."
Version
Author's final manuscript
Abstract
© 2018 IEEE. This paper demonstrates accurate human pose estimation through walls and occlusions. We leverage the fact that wireless signals in the WiFi frequencies traverse walls and reflect off the human body. We introduce a deep neural network approach that parses such radio signals to estimate 2D poses. Since humans cannot annotate radio signals, we use state-of-the-art vision model to provide cross-modal supervision. Specifically, during training the system uses synchronized wireless and visual inputs, extracts pose information from the visual stream, and uses it to guide the training process. Once trained, the network uses only the wireless signal for pose estimation. We show that, when tested on visible scenes, the radio-based system is almost as accurate as the vision-based system used to train it. Yet, unlike vision-based pose estimation, the radio-based system can estimate 2D poses through walls despite never trained on such scenarios. Demo videos are available at our website.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/cvpr.2018.00768
