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

Related tags

Deep Learningpythonpytorchcross-domaingaitgait-recognitioncasia-boulp
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

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