Aggregate Together We See Through:
WiFi-based Through-wall2D Human Pose Estimation via Multi-rate Directed T-GCN
| Place | Type | Url | Place | Type | Url |
|---|---|---|---|---|---|
| P1 | single-person 01 | https://reurl.cc/oLE0bV | P1 | single-person 02 | https://reurl.cc/E75GNa |
| P1 | single-person 03 | https://reurl.cc/MvKRmm | P1 | single-person 04 | https://reurl.cc/ZOLWjM |
| P1 | single-person 05 | https://reurl.cc/z8nYW6 | P1 | single-person 06 | https://reurl.cc/1xN1oV |
| P1 | multi-people | https://reurl.cc/9EmVA8 | P1 | wall | https://reurl.cc/WdKDY5 |
| P2 | single-person 01 | https://reurl.cc/5l9MyR | P2 | single-person 02 | https://reurl.cc/62nN3k |
| P2 | single-person 03 | https://reurl.cc/j7Klj2 | P2 | single-person 04 | https://reurl.cc/b5K9V6 |
| P2 | single-person 05 | https://reurl.cc/3DqxXL | P2 | single-person 06 | https://reurl.cc/exKDzm |
| P2 | multi-people | https://reurl.cc/4RVQ7V |
Our demo for wifi based 2D human pose estimation
| Proposed Model | Person-in-WiFi[2] |
|---|---|
 |
 |
multi-people
single-person
The PCK@20(Percentage of Correct Keypoint)of provided models are shown here:
| Method | single-person | multi-people |
|---|---|---|
| WiSPPN[1] | 69.82% | X |
| Person-in-WiFi[2] | 77.06% | 61.58% |
| MDT-GCN(ours) | 82.26% | 71.58% |
| Method | through-wall |
|---|---|
| WiSPPN[1] | 58.86% |
| Person-in-WiFi[2] | 73.67% |
| MDT-GCN(ours) | 80.72% |
Gap between Camera-based(Openpose) and Labeld Ground Truth.(Ramdomly sample 100 examples)
| Camera-based(Openpose) |
|---|
| 100% |
[1] Fei Wang, Stanislav Panev, Ziyi Dai, Jinsong Han, and Dong Huang. 2019. Canwifi estimate person pose?arXiv preprint arXiv:1904.00277(2019).
[2] Fei Wang, Sanping Zhou, Stanislav Panev, Jinsong Han, and Dong Huang. 2019.Person-in-WiFi: Fine-grained person perception using WiFi. InProceedings of theIEEE International Conference on Computer Vision. 5452–5461.