Check out our new version GaitGraph2!

With support for OUMVLP-Pose Dataset, In-Memory Datasets & PyTorch Lightning

Towards a Deeper Understanding of Skeleton-based Gait Recognition

This repository contains the PyTorch code for:

GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition

Torben Teepe, Ali Khan, Johannes Gilg, Fabian Herzog, Stefan Hörmann

• Python >= 3.6
• CUDA >= 10

First, create a virtual environment or install dependencies directly with:

pip3 install -r requirements.txt

The extraction of the pose data from CASIA-B can either run the commands bellow or download the preprocessed data using:

cd data
sh ./download_data.sh

Optional: If you choose to run the preprocessing, download the dataset and run the following commands.

# Download required weights
cd models
sh ./download_weights.sh

# Copy extraction script
# <PATH_TO_CASIA-B> should be something like: /home/ ... /datasets/CASIA_Gait_Dataset/DatasetB
cd ../data
cp extract_frames.sh <PATH_TO_CASIA-B>

cd <PATH_TO_CASIA-B>
mkdir frames
sh extract_frames.sh
cd frames
find . -type f -regex ".*\.jpg" -print | sort | grep -v bkgrd > ../casia-b_all_frames.csv
cp ../casia-b_all_frames.csv <PATH_TO_REPO>/data

cd <PATH_TO_REPO>/src
export PYTHONPATH=${PWD}:$PYTHONPATH

cd preparation
python3 prepare_detection.py <PATH_TO_CASIA-B> ../../data/casia-b_all_frames.csv ../../data/casia-b_detections.csv
python3 prepare_pose_estimation.py  <PATH_TO_CASIA-B> ../../data/casia-b_detections.csv ../../data/casia-b_pose_coco.csv
python3 split_casia-b.py ../../data/casia-b_pose_coco.csv --output_dir ../../data

To train the model you can run the train.py script. To see all options run:

cd src
export PYTHONPATH=${PWD}:$PYTHONPATH

python3 train.py --help

Check experiments/1_train_*.sh to see the configurations used in the paper.

Optionally start the tensorboard with:

tensorboard --logdir=save/casia-b_tensorboard 

Evaluate the models using evaluate.py script. To see all options run:

python3 evaluate.py --help

Top-1 Accuracy per probe angle excluding identical-view cases for the provided models on CASIA-B dataset.

The pre-trained model is available here.

GaitPose itself is released under the MIT License (see LICENSE).

The following parts of the code are borrowed from other projects. Thanks for their wonderful work!

• Object Detector: eriklindernoren/PyTorch-YOLOv3
• Pose Estimator: HRNet/HRNet-Human-Pose-Estimation
• ST-GCN Model: yysijie/st-gcn
• ResGCNv1 Model: yfsong0709/ResGCNv1
• SupCon Loss: HobbitLong/SupContrast

If you use GaitGraph, please use the following BibTeX entry.

@inproceedings{teepe2021gaitgraph,
  author={Teepe, Torben and Khan, Ali and Gilg, Johannes and Herzog, Fabian and H\"ormann, Stefan and Rigoll, Gerhard},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={Gait{G}raph: Graph Convolutional Network for Skeleton-Based Gait Recognition}, 
  year={2021},
  pages={2314-2318},
  doi={10.1109/ICIP42928.2021.9506717}
}