2019/09/20 by Tianhong Li, Li, Tianhong, Lijie Fan +8
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Gait Recognition and Analysis #Human Pose and Action Recognition #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Video Surveillance and Tracking Methods #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1909.09300
ICCV 2019. The first two authors contributed equally to this paper
arxiv created 2019/09/20 · openalex publication_date 2019/09/20 · arxiv updated 2019/09/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.