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2s-AGCN

An unofficial Tensorflow implementation of the paper "Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition" in CVPR 2019.

Model Weights

Model weights for ST-GCN trained on xview and xsub joint data Dropbox

Dependencies

  • Python >= 3.5
  • scipy >= 1.3.0
  • numpy >= 1.16.4
  • tensorflow >= 2.0.0

Directory Structure

Most of the interesting stuff can be found in:

  • model/agcn.py: model definition of AGCN
  • data_gen/: how raw datasets are processed into numpy tensors
  • graphs/ntu_rgb_d.py: graph definition
  • main.py: general training/eval processes; etc.

Downloading & Generating Data

NTU RGB+D

  1. The NTU RGB+D dataset can be downloaded from here. We'll only need the Skeleton data (~ 5.8G).

  2. After downloading, unzip it and put the folder nturgb+d_skeletons to ./data/nturgbd_raw/.

  3. Generate the joint dataset first:

cd data_gen
python3 gen_joint_data.py

Specify the data location if the raw skeletons data are placed somewhere else. The default looks at ./data/nturgbd_raw/.

  1. Then, in data_gen/, generate the bone dataset:
python3 gen_bone_data.py
  1. Generate the tfrecord files for motion and spatial data :
python3 gen_tfrecord_data.py

The generation scripts look for generated data in previous step. By default they look at ./data; change dir configs if needed.

Training

To start training the network with the joint data, use the following command:

python3 main.py --train-data-path data/ntu/<dataset folder> --test-data-path data/ntu/<dataset folder>

Here refers to the folder containing the tfrecord files generated in step 5 of the pre-processing steps.

Note: At the moment, only nturgbd-cross-subject is supported.

Citation

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

@inproceedings{2sagcn2019cvpr,  
      title     = {Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition},  
      author    = {Lei Shi and Yifan Zhang and Jian Cheng and Hanqing Lu},  
      booktitle = {CVPR},  
      year      = {2019},  
}

@article{shi_skeleton-based_2019,
    title = {Skeleton-{Based} {Action} {Recognition} with {Multi}-{Stream} {Adaptive} {Graph} {Convolutional} {Networks}},
    journal = {arXiv:1912.06971 [cs]},
    author = {Shi, Lei and Zhang, Yifan and Cheng, Jian and LU, Hanqing},
    month = dec,
    year = {2019},
}