WiSLAR

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code of Temporal Unet

WiSLAR 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Temporal Unet: Sample Level Action Recognition using WiFi

Data is [here](https://drive.google.com/open?id=1f2SrtotfBlWBrQIRRH-aM56cmJ2Tq9Iw), pre-trained model on action detection is [here](https://drive.google.com/open?id=1z4AEUkv5s9-d21xEJRF274qfkKIby6ts), and pre-trained model on action classification is [here](https://drive.google.com/open?id=1B3jZwoF5dmHCYGlwrf_LL2zSNY_pyzXL).

## Data description
### **train_data.mat**
1. train_data_amp: CSI amplitude of training data: 1116x52x192, 1116 CSI series, each series has 52 carriers, and 192 samples
2. train_label_instance: action labels, 1116x192, 1116 CSI series, each series and 192 samples that are labeld from 0 to 6 (background + 6 actions).
3. train_label_mask: action labels, 1116x192, 1116 CSI series, each series has 192 samples that are binaryly labeled.
4. train_label_time: start time and end time labels. 1116x2, 2 is for start and end.

### **test_data.mat**
similar to the above

## Network
Temporal operations including 1d convolutions, 1D maxpoolings, and 1D deconvolutions sweep along the time axis of CSI series.
![network](https://raw.githubusercontent.com/geekfeiw/WiSLAR/master/figs/unet1d.png)

## AP Curves
![network](https://raw.githubusercontent.com/geekfeiw/WiSLAR/master/figs/ap.png)

## An example
![network](https://raw.githubusercontent.com/geekfeiw/WiSLAR/master/figs/result.png)

If this helps in your research, please kindly cite,

```
@article{wang2019temporal,
  title={Temporal Unet: Sample Level Human Action Recognition using WiFi},
  author={Wang, Fei and Song, Yunpeng and Zhang, Jimuyang and Han, Jinsong and Huang, Dong},
  journal={arXiv preprint arXiv:1904.11953},
  year={2019}
}
```

WiSLAR

WiSLAR 최신버젼 다운로드

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WiSLAR 최신버젼 다운로드

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