SGP-JCA

Introduction

This repository holds the codebase for the paper:

Graph convolutional network with structure pooling and joint-wise channel attention for action recognition Chen, Yuxin and Ma, Gaoqun and Yuan, Chunfeng and Li, Bing and Zhang, Hui and Wang, Fangshi and Hu, Weiming, Pattern Recognition 2020. paper

Prerequisties

• Python3.6
• Pytorch1.2.0

Installation

git clone https://github.com/Uason-Chen/SGP-JCA.git
cd SGP-JCA
pip install -e torchlight

Data Preparation

NTU-RGB+D

NTU-RGB+D can be downloaded from link. Only the 3D skeletons (5.8G) modality is required in our experiments. After that, run the following command to build the dataset for training or evaluation:

cd tools
python ntu_gendata.py --data_path <path to nturgbd_skeletons_s001_to_s017.zip>

Training

python main.py --config config/sgp+jca/<dataset>/train.yaml --work-dir <work folder>

where <dataset> can be nturgbd-cross-subject or nturgbd-cross-view. The training results, including model weights, configurations and logging files, will be saved under <work folder>.

Evaluation

python main.py --config <work folder>/config.yaml --phase test --work-dir <work folder> --weights <work folder>/<weights>

where <weights> is the model weights ended with .pt. For example, the provided pre-trained model on NTU-RGB+D Cross Subject can be evaluated by running the following command:

python main.py --config config/sgp+jca/nturgbd-cross-subject/test.yaml --phase test --work-dir ./weights --weights ./weights/ntucs.pt

Citation

Please cite the following paper if you use this repository in your research.

@article{chen2020graph,
  title={Graph convolutional network with structure pooling and joint-wise channel attention for action recognition},
  author={Chen, Yuxin and Ma, Gaoqun and Yuan, Chunfeng and Li, Bing and Zhang, Hui and Wang, Fangshi and Hu, Weiming},
  journal={Pattern Recognition},
  pages={107321},
  year={2020},
  publisher={Elsevier}
}