TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition.

Last update: Apr 04, 2022

Overview

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

Download CASIA-B and OU-LP

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}
}

Acknowledgement

GaitSet
AND

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Releases(V0.1)

V0.1(Sep 27, 2021)
CASIA_best_encoder.ptm: The best model of GaitSet model trained on the CASIA-B dataset.

OULP_best_encoder.ptm: The best model of GaitSet model trained on the OU-LP dataset.

CASIA2OULP_best_encoder.ptm: The best model of TraND model trained on the CASIA-B dataset and tested on the OU-LP dataset.

OULP2CASIA_best_encoder.ptm: The best model of TraND model trained on the OU-LP dataset and tested on the CASIA-B dataset.

Source code(tar.gz)
Source code(zip)
CASIA2OULP_best_encoder.ptm(24.08 MB)
CASIA_best_encoder.ptm(24.08 MB)
OULP2CASIA_best_encoder.ptm(24.08 MB)
OULP_best_encoder.ptm(24.08 MB)
segmentation_hrnet_best_model.pth(251.88 MB)

TraND: Transferable Neighborhood Discovery for Unsupervised Cross-domain Gait Recognition.

Related tags

Overview

TraND

Requirements

Installation

Data Preparation

Data Pretreatment

Data Structrue

Train

Stage I: Supervised Prior Knowledge Learning on Source Domain

Stage II: Transferable Neighbor Discovery on Target Domain

Test

Citation

Acknowledgement

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Releases(V0.1)

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Owner

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