Skip to content

The implementation for "Skeleton-Based Action Recognition with Shift Graph Convolutional Network" (CVPR2020 oral).

License

Notifications You must be signed in to change notification settings

kchengiva/Shift-GCN

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

7 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Shift-GCN

The implementation for "Skeleton-Based Action Recognition with Shift Graph Convolutional Network" (CVPR2020 oral). Shift-GCN is a lightweight skeleton-based action recognition model, which exceeds state-of-the-art methods with 10x less FLOPs.

image

Prerequisite

  • PyTorch 0.4.1
  • Cuda 9.0
  • g++ 5.4.0

Compile cuda extensions

cd ./model/Temporal_shift
bash run.sh

Data Preparation

  • Download the raw data of NTU-RGBD and NTU-RGBD120. Put NTU-RGBD data under the directory ./data/nturgbd_raw. Put NTU-RGBD120 data under the directory ./data/nturgbd120_raw.

  • For NTU-RGBD, preprocess data with python data_gen/ntu_gendata.py. For NTU-RGBD120, preprocess data with python data_gen/ntu120_gendata.py.

  • Generate the bone data with python data_gen/gen_bone_data.py.

  • Generate the motion data with python data_gen/gen_motion_data.py.

Training & Testing

  • NTU X-view

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

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

    python main.py --config ./config/nturgbd-cross-view/train_joint_motion.yaml

    python main.py --config ./config/nturgbd-cross-view/train_bone_motion.yaml

  • NTU X-sub

    python main.py --config ./config/nturgbd-cross-subject/train_joint.yaml

    python main.py --config ./config/nturgbd-cross-subject/train_bone.yaml

    python main.py --config ./config/nturgbd-cross-subject/train_joint_motion.yaml

    python main.py --config ./config/nturgbd-cross-subject/train_bone_motion.yaml

  • For NTU120, change the dataset path in config files, and change num_class in config files from 60 to 120.

Multi-stream ensemble

To ensemble the results of 4 streams. Change models name in ensemble.py depending on your experiment setting. Then run python ensemble.py.

Trained models

We release several trained models:

Model Dataset Setting Top1(%)
./save_models/ntu_ShiftGCN_joint_xview.pt NTU-RGBD X-view 95.1
./save_models/ntu_ShiftGCN_joint_xsub.pt NTU-RGBD X-sub 87.8
./save_models/ntu120_ShiftGCN_joint_xsetup.pt NTU-RGBD120 X-setup 83.2
./save_models/ntu120_ShiftGCN_joint_xsub.pt NTU-RGBD120 X-sub 80.9

Citation

If you find this model useful for your research, please use the following BibTeX entry.

@inproceedings{cheng2020shiftgcn,  
  title     = {Skeleton-Based Action Recognition with Shift Graph Convolutional Network},  
  author    = {Ke Cheng and Yifan Zhang and Xiangyu He and Weihan Chen and Jian Cheng and Hanqing Lu},  
  booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},  
  year      = {2020},  
}

About

The implementation for "Skeleton-Based Action Recognition with Shift Graph Convolutional Network" (CVPR2020 oral).

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published