Awesome

STTFormer

This repository is the official implementation for Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition.

Architecture of STTFormer

Data Preparation

Download datasets.

There are 2 datasets to download:

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

NTU RGB+D 60 and 120

1. Request dataset here: https://rose1.ntu.edu.sg/dataset/actionRecognition/
2. Download the skeleton-only datasets:
   1. nturgbd_skeletons_s001_to_s017.zip (NTU RGB+D 60)
   2. nturgbd_skeletons_s018_to_s032.zip (NTU RGB+D 120)
   3. Extract above files to ./gendata/nturgbd_raw

Data Processing

Directory Structure

Put downloaded data into the following directory structure:

- gendata/
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons/     # from `nturgbd_skeletons_s001_to_s017.zip`
      ...
    - nturgb+d_skeletons120/  # from `nturgbd_skeletons_s018_to_s032.zip`
      ...

Generating Data

• Generate NTU RGB+D 60 or NTU RGB+D 120 dataset:

 cd ./gendata/ntu # or cd ./gendata/ntu120
 # Get skeleton of each performer
 python3 get_raw_skes_data.py
 # Remove the bad skeleton 
 python3 get_raw_denoised_data.py
 # Transform the skeleton to the center of the first frame
 python3 seq_transformation.py

Training & Testing

Training

• Change the config file depending on what you want.

• To train model on NTU RGB+D 60/120 with joint, bone or motion modalities, run the following command.

# Example: training STTFormer on NTU RGB+D 60 cross subject under bone modality
python3 main.py --config config/ntu60_xsub_bone.yaml

Testing

• To test the trained models saved in <work_dir>, run the following command:

# Example: testing STTFormer on NTU RGB+D 60 cross subject under bone modality
python3 main.py --config config/ntu60_xsub_bone.yaml --run_mode test --save_score True --weights work_dir/ntu60/xsub_bone/xsub_bone.pt

As with training, it should be noted that the data modality should correspond to the weight.

• To ensemble the results of different modalities, run

# Example: ensemble three modalities of STTFormer on NTU RGB+D 60 cross subject
python3 ensemble.py --dataset ntu/xsub --joint_dir work_dir/ntu60/xsub_joint --bone_dir work_dir/ntu60/xsub_bone --joint_motion_dir work_dir/ntu60/xsub_joint_motion

Pretrained Models

• The pretrained models will be available soon.

Acknowledgements

This repository is based on CTR-GCN. Thanks to the original authors for their work!

Citation

Please cite this work if you find it useful:.

  @article{Qiu2022SpatioTemporalTT,
    title={Spatio-Temporal Tuples Transformer for Skeleton-Based Action Recognition},
    author={Helei Qiu and Biao Hou and Bo Ren and Xiaohua Zhang},
    journal={ArXiv},
    year={2022},
    volume={abs/2201.02849}
  }