Papers › Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Making the Invisible Visible: Action Recognition Through Walls and Occlusions
Tianhong Li, Lijie Fan, Ming-Min Zhao, Yingcheng Liu, Dina Katabi
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| RF-based Pose Estimation | RF-MMD | RF-Action | mAP (@0.1, Through-wall) | 86.5 | #1 of 1 | Archive leaderboard | report |
| RF-based Pose Estimation | RF-MMD | RF-Action | mAP (@0.1, Visible) | 90.1 | #1 of 1 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | RF-Action | Accuracy (CS) | 86.8 | #80 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | NTU RGB+D | RF-Action | Accuracy (CV) | 91.6 | #80 of 135 | Archive leaderboard | report |
| Skeleton Based Action Recognition | PKU-MMD | RF-Action | mAP@0.50 (CS) | 92.9 | #1 of 4 | Archive leaderboard | report |
| Skeleton Based Action Recognition | PKU-MMD | RF-Action | mAP@0.50 (CV) | 94.4 | #1 of 4 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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