Making the Invisible Visible: Action Recognition Through Walls and Occlusions


Tianhong Li*      Lijie Fan*      Mingmin Zhao      Yingcheng Liu      Dina Katabi
Massachusetts Institute of Technology
(*: First two authors contributed equally;)


Overview:


Understanding people’s actions and interactions typically depends on seeing them. But what if it is too dark, or if the person is behind a wall? We introduce a model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons, and recognizes actions and interactions of multiple people over time. Our model can learn from both vision-based and RF-based datasets; it achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today’s vision-based action recognition.


Demo:


Real-time Through-wall Human Activity Recognition using Radio Signals
Tianhong Li*, Lijie Fan*, Mingmin Zhao, Dina Katabi
European Conference on Computer Vision (ECCV) 2020, Demo Track
[Website] [Demo Video]


Video:




Paper:




Also check out:


RF-Based 3D Skeletons
M. Zhao, Y. Tian, H. Zhao, M. Alsheikh, T. Li, R. Hristov, Z. Kabelac, D. Katabi and A. Torralba
ACM SIGCOMM, 2018

Through-Wall Human Pose Estimation using Radio Signals
M. Zhao, T. Li, M. Alsheikh, Y. Tian, H. Zhao, A. Torralba and D. Katabi
Computer Vision and Pattern Recognition (CVPR), 2018