Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition

This repo contains the official implementation for Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition. The paper is accepted to DAGM / GCPR 2023.

Main Idea

Prerequisites

We use the same prerequisites as CTR-GCN

• Python >= 3.6
• PyTorch >= 1.1.0
• PyYAML, tqdm, tensorboardX

Further dependencies:

• Run pip install -r requirements.txt
• Run pip install -e torchlight

To install Torchlight:

cd ../graph/torchlight; python setup.py install
cd ../torchlight; python setup.py install
pip install -e torchlight

Data

1. NTU RGB+D 120 Action Recognition Dataset: https://github.com/shahroudy/NTURGB-D

2. First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose Annotations: https://guiggh.github.io/publications/first-person-hands/

Training

• Select the config file depending on which dataset and modality you are interested in.
• Select the model that you are interested in running. You can create your own model files under ./model.

# Example: training model LSHT_4 on NTU RGB+D 120 on the cross-subject dataset using the joint modality with GPU 0
python main.py --config config/nturgbd120-cross-subject/default.yaml --model model.LSHT_4.Model --work-dir work_dir/ntu120/csub/LSHT --device 0

Testing

• To test a trained model saved in <work_dir>, run this command:

python main.py --config <work_dir>/config.yaml --work-dir <work_dir> --phase test --save-score True --weights <work_dir>/xxx.pt --device 0

• To ensemble the results of different modalities, run

python ensemble.py --datasets ntu120/xsub --joint-dir work_dir/ntu120/csub/LSHT_4 --bone-dir work_dir/ntu120/csub/LSHT_4_bone --joint-motion-dir work_dir/ntu120/csub/LSHT_4_vel --bone-motion-dir work_dir/ntu120/csub/LSHT_4_bone_vel

• To calculate hand accuracy, run

python hand_action_acc.py --datasets ntu120/xsub --acc-dir work_dir/ntu120/csub/LSHT_4 --best_ep 64

Acknowledgements

This repo is based on CTR-GCN. The data processing is borrowed from SGN and HCN.

Thank you to the original authors for their work!

Citation

Please cite our paper if you find it useful:

@article{prasse2023local,
  title={Local Spherical Harmonics Improve Skeleton-Based Hand Action Recognition},
  author={Prasse, Katharina and Jung, Steffen and Zhou, Yuxuan and Keuper, Margret},
  journal={arXiv preprint arXiv:2308.10557},
  year={2023}
}

Contact

For any questions, feel free to contact: katharina.prasse@uni-siegen.de