Angular Encoding for Skeleton-Based Action Recognition

Overview

PyTorch implementation of "TNNLS 2022: Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition". (https://arxiv.org/pdf/2105.01563.pdf).

Angular Features

Network Architecture

Dependencies

Python >= 3.6
PyTorch >= 1.2.0
NVIDIA Apex (auto mixed precision training)
PyYAML, tqdm, tensorboardX, matplotlib, seaborn

Data Preparation

Download Datasets

There are 2 datasets to download:

NTU RGB+D 60 Skeleton
NTU RGB+D 120 Skeleton

Request the datasets here: http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp

Data Preprocessing

Directory Structure

Put downloaded data into the following directory structure:

- data/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`

Generating Data

cd data_gen
python3 ntu_gendata.py
python3 ntu120_gendata.py
This can take hours. Better CPUs lead to much faster processing.

Training

bash train.sh

Testing

bash test.sh

Acknowledgements

This repo is based on

Thanks to the original authors for their work!

The flat icon is from Freepik.

Citation

Please cite this work if you find it useful:

@article{DBLP:journals/corr/abs-2105-01563,
  author    = {Zhenyue Qin and Yang Liu and Pan Ji and Dongwoo Kim and Lei Wang and
               Bob McKay and Saeed Anwar and Tom Gedeon},
  title     = {Fusing Higher-Order Features in Graph Neural Networks for Skeleton-based Action Recognition},
  journal   = {IEEE Transactions on Neural Networks and Learning Systems (TNNLS)},
  year      = {2022}
}

Contact

If you have further question, please email zhenyue.qin@anu.edu.au or yang.liu3@anu.edu.au.

zangtuo/Angular-Skeleton-Encoding