Seeing the Unseen:

Wifi-based 2D Human Pose Estimation via Evolving Attentive Spatial-Frequency Network

• Architecture

• Dataset

• Usage

• Visulization

• Result

• Reference

Architecture

Dataset

GPE			SPE
# Person	Number	Url	Action	Number	Url
1	12,884	https://reurl.cc/EzXD8R	Walk	78,852	https://reurl.cc/OqEL9D
2	18,879	https://reurl.cc/ygmX1a	Wave	77,431	https://reurl.cc/Z71vGQ
3	27,694	https://reurl.cc/e8Wa5W	Jump	40,670	https://reurl.cc/j5RYNM
>=4	28,178	https://reurl.cc/m9ZXd7	Run	41,238	https://reurl.cc/EzXL3m
Cross Period	X	X	Cross Period	X	https://reurl.cc/ld4mZq
Total	87,635	X	Total	238,191	X

Usage

Install

$ git clone https://github.com/fingerk28/EASFN.git

Setup

create the virtual enciorment

$ cd EASFN/
$ python3 -m venv env

activate the virtual environment

$ source env/bin/activate

install the necessary packages (If there are errors about gdown package, you can ignore them.)

$ pip install -r requirements.txt

Download the dataset

Download SPE or GPE dataset

$ chmod +x download_SPE.sh      
$ ./download_SPE.sh

$ chmod +x download_GPE.sh 
$ ./download_GPE.sh

When you find the download has exited, you need to press ctrl+c to terminate the sh file by yourself.

If errors occur, you can download by yourself using the links provided above.

Please make sure that all four zipfiles in EASFN/dataset/SPE or EASFN/dataset/GPE.

• unzip the dataset ( You can substitute GPE for SPE)

$ cd EASFN/dataset/SPE
$ chmod +x SPE.sh
$ ./SPE.sh

Train

You can adjust the training parameters in config/args.py

$ python3 train.py--dataset=SPE

$ python3 train.py --dataset=GPE

Test

You can adjust the testing parameters in config/args.py

$ python3 test.py --dataset=SPE   

$ python3 test.py --dataset=GPE

Visulization

Comparative visualization of EASFN and two models (PIW[2] and WiSPPN[1])

Some positive cases of EASFN on our two benchmarks

Comparative GIF images of EASFN and two models (PIW[2] and WiSPPN[1]) for each action

	EASFN	PIW	WiSPPN
Walk
Wave
Jump
Run

Comparative visualization of EASFN and camera-based method(Openpose)

From this comparison, we can easily observe that EASFN is better than the camera-based method in dark environment. Under poor illumination, Openpose is likely to loss some keypoints, such as wrist, ankle, and so forth. However, EASFN can correctly return all keypoints.

Result

PCK curve of each model:

PCK of EASFN on each joint:

• Comparisons on the proposed benchmarks:

Metric	Benchmark	WiSPPN[1]	PIW[2]	EASFN
MPJPE	SPE	44.16	78.88	37.34
	GPE	X	119.60	44.14
PCK@20	SPE	21.86%	32.96%	50.05%
	GPE	X	27.64%	43.98%

• Comparative results against other methods on our SPE benchmark (PCK@20):

Action	WiSPPN	PIW	EASFN
Walk	23.58%	39.87%	61.14%
Wave	25.92%	33.06%	45.81%
Run	22.12%	37.91%	58.11%
Jump	15.82%	20.99%	35.15%

• Comparative results against other methods on our GPE benchmark (PCK@20):

# Person	PIW	EASFN
1-person	53.37%	72.65%
2-person	49.31%	65.39%
3-person	19.54%	34.75%
>=4 person	22.66%	39.12%

• Ablation study on our SPE benchmark:

Model	Architecture	PCK@20
1	SFN	44.99%
2	ASFN	45.95%
3	EASFN, D=1	48.08%
4	EASFN, D=3 (Proposed)	50.05%
5	EASFN, D=5	45.95%

Reference

• [1] Fei Wang, Stanislav Panev, Ziyi Dai, Jinsong Han, and Dong Huang. 2019. Can wifi 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 the IEEE International Conference on Computer Vision. 5452–5461.