TraND
This is the code for the paper "Jinkai Zheng, Xinchen Liu, Chenggang Yan, Jiyong Zhang, Wu Liu, Xiaoping Zhang and Tao Mei: TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition. ISCAS 2021"
Requirements
- Conda
- GPUs
- Python 3.7
- PyTorch 1.1.0
Installation
You can replace the second command from the bottom to install pytorch based on your CUDA version.
git clone https://github.com/JinkaiZheng/TraND.git
cd TraND
conda create --name py37torch110 python=3.7
conda activate py37torch110
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
pip install -r requirements
Data Preparation
Data Pretreatment
pretreatment_casia.py and pretreatment_oulp.py use the alignment method in this paper. In the case of CASIA-B dataset, you need to run the command:
python GaitSet/pretreatment_casia.py --input_path='root_path_of_raw_dataset' --output_path='./data/CASIA-B'
Data Structrue
After the pretreatment, the data structure under the directory should like this
./data
├── CASIA-B
│ ├── 001
│ ├── bg-01
│ ├── 000
│ └── 001-bg-01-000-001.png
├── OULP
│ ├── 0000024
│ ├── Seq00
│ ├── 55
└── 00000061.png
Train
Stage I: Supervised Prior Knowledge Learning on Source Domain
Training the GaitSet model in the source domain, run this command:
python GaitSet/train.py --data "casia-b"
Stage II: Transferable Neighbor Discovery on Target Domain
Fine-tuning the GaitSet model in the target domain with TraND method, run this command:
sh Experement.sh
Test
Testing the model in self domain, such as CASIA-B dataset, run this command:
python GaitSet/test.py --data "casia-b"
Testing the model in cross domain, such as CASIA-B -> OU-LP dataset, run this command:
python GaitSet/test_cross.py --source "casia-b" --target "oulp"
Citation
Please cite this paper in your publications if it helps your research:
@article{DBLP:journals/corr/abs-2102-04621,
author = {Jinkai Zheng and
Xinchen Liu and
Chenggang Yan and
Jiyong Zhang and
Wu Liu and
Xiaoping Zhang and
Tao Mei},
title = {TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain
Gait Recognition},
journal = {ISCAS},
year = {2021}
}
87 Nov 30, 2022
148 Nov 30, 2022
189 Dec 25, 2022
66 Aug 13, 2022
52 Jan 7, 2023
46 Nov 17, 2022
23 Dec 21, 2022
10 Dec 12, 2022
18 Sep 10, 2022
130 Jan 02, 2023
89 Dec 20, 2022
3 Jun 27, 2022
44 Dec 09, 2022
2 Jan 11, 2022
40 Sep 18, 2022
2 Dec 16, 2021
1 Jan 10, 2022
27 Dec 02, 2022
38 Sep 16, 2022
21 Nov 14, 2022
24 May 23, 2022
97 Jan 05, 2023
82 Dec 15, 2022
1 Jan 28, 2022
8.3k Dec 31, 2022
26 Nov 29, 2022
16 Dec 14, 2022
12 Jul 22, 2022
108 Dec 20, 2022