Through-Wall Human Pose Estimation Using Radio Signals

Mingmin Zhao      Tianhong Li      Mohammad Abu Alsheikh      Yonglong Tian      Hang Zhao      Rumen Hristov
Antonio Torralba      Dina Katabi

Massachusetts Institute of Technology


RF-Pose provides accurate human pose estimation through walls and occlusions. It leverages the fact that wireless signals in the WiFi frequencies traverse walls and reflect off the human body. It uses a deep neural network approach that parses such radio signals to estimate 2D poses. RF-Pose is trained using state-of-the-art vision model to provide cross-modal supervision. Once trained, RF-Pose uses only the wireless signal for pose estimation. Experimental results 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.



Through-Wall Human Pose Estimation Using Radio Signals
Mingmin Zhao, Tianhong Li, Mohammad Abu Alsheikh, Yonglong Tian, Hang Zhao, Antonio Torralba, Dina Katabi
Computer Vision and Pattern Recognition (CVPR), 2018


Coming soon.

Also check out:

RF-Capture: Capturing the Human Figure Through a Wall
Fadel Adib, Chen-Yu Hsu, Hongzi Mao, Dina Katabi, Fredo Durand
SIGGRAPH Asia 2015