Part-based Graph Convolutional Network for Skeleton-based Action Recognition
Official repository for the code from BMVC (British Machine Vision Conference) paper "Part-based Graph Convolutional Network for Action Recognition". The implementation is done in Pytorch and works on it's recent stable version. The repository includes:
- Code for the final model used in the paper.
- Model checkpoints for model trained on NTURGB+D Cross Subject and Cross View data splits.
- Training and testing config as well as data preparation scripts for NTURGB+D dataset.
- Training config as well as data preparation scripts for HDM05 dataset.
TODOs:
- Code for visualizing results.
- Document how to extend the code for other projects.
Getting Started
- Download the NTURGB+D dataset (with 60 action classes) following this link. Unzip the archive and store all the skeleton files in a single directory:
unzip nturgbd_skeletons_s001_to_s017.zip -d nturgb+d_skeletons
- Clone the repository:
git clone https://github.com/kalpitthakkar/pb-gcn.git
- Download the pretrained model checkpoints. To download the checkpoints for both the cross-subject and cross-view splits:
bash download_checkpoints.sh <path_to_download_directory>
Training / Testing Models
-
First of all, we need to define the required configuration variables in the YAML file. The instructions on it's structure and editing the file are here.
-
Once the configuration file is ready, you can start training the model:
python run.py --config <path_to_YAML_config_file>
Citation
For citing our paper:
@article{thakkar2018part,
title={Part-based Graph Convolutional Network for Action Recognition},
author={Thakkar, Kalpit and Narayanan, PJ},
journal={arXiv preprint arXiv:1809.04983},
year={2018}
}