Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Name
1909.09300.pdf
Description
Submitted version
Size
6 MB
Format
Adobe PDF
Checksum (MD5)
4bf310e0048dce2913bdfcd0a50feaa1
Author(s)
Li, Tianhong
Fan, Lijie
Zhao, Mingmin
Liu, Yingcheng
Katabi, Dina
Date Issued
February 2020
Journal
2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Li, Tianhong et al. "Making the Invisible Visible: Action Recognition Through Walls and Occlusions." 2019 IEEE/CVF International Conference on Computer Vision (ICCV), October-November 2019, Seoul, Korea, Institute of Electrical and Electronics Engineers, February 2020. © 2019 IEEE
Version
Original manuscript
Abstract
Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network 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 as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/iccv.2019.00096
