GaitGraph

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

Quick Start

Prerequisites

Python >= 3.6
CUDA >= 10

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

pip3 install -r requirements.txt

Data preparation

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

Train

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

Evaluation

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

python3 evaluate.py --help

Main Results

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

	0	18	36	54	72	90	108	126	144	162	180	mean
NM#5-6	85.3	88.5	91	92.5	87.2	86.5	88.4	89.2	87.9	85.9	81.9	87.7
BG#1-2	75.8	76.7	75.9	76.1	71.4	73.9	78	74.7	75.4	75.4	69.2	74.8
CL#1-2	69.6	66.1	68.8	67.2	64.5	62	69.5	65.6	65.7	66.1	64.3	66.3

The pre-trained model is available here.

Licence & Acknowledgement

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

Citing GaitGraph

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

@misc{teepe2021gaitgraph,
      title={GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition}, 
      author={Torben Teepe and Ali Khan and Johannes Gilg and Fabian Herzog and Stefan H\"ormann and Gerhard Rigoll},
      year={2021},
      eprint={2101.11228},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

MritulaC/GaitGraph