DAGCN

Note

Now we have updated the code to >=Pytorch0.4.
A new model named AAGCN is added, which can achieve better performance.

Data Preparation

• Download the raw data from NTU-RGB+D and Skeleton-Kinetics. Then put them under the data directory:

   -data\  
     -kinetics_raw\  
       -kinetics_train\
         ...
       -kinetics_val\
         ...
       -kinetics_train_label.json
       -keintics_val_label.json
     -nturgbd_raw\  
       -nturgb+d_skeletons\
         ...
       -samples_with_missing_skeletons.txt
  

• Preprocess the data with

  python data_gen/ntu_gendata.py

  python data_gen/kinetics-gendata.py.

• Generate the bone data with:

  python data_gen/gen_bone_data.py

Training & Testing

Change the config file depending on what you want.

`python main.py --config ./config/nturgbd-cross-view/train_joint.yaml`

`python main.py --config ./config/nturgbd-cross-view/train_bone.yaml`

To ensemble the results of joints and bones, run test firstly to generate the scores of the softmax layer.

`python main.py --config ./config/nturgbd-cross-view/test_joint.yaml`

`python main.py --config ./config/nturgbd-cross-view/test_bone.yaml`

Then combine the generated scores with:

`python ensemble.py` --datasets ntu/xview

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},
}