Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19
PyTorch version should be 0.3! For PyTorch0.4 or higher, the codes need to be modified. \
Now we have updated the code to >=Pytorch0.4. \
A new model named AAGCN is added, which can achieve better performance.
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
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
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},
}
For any questions, feel free to contact: lei.shi@nlpr.ia.ac.cn