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SkelAct

SkelAct is an open source repository which provides state-of-the-art skeleton-based action recognition models from Hikvision Research Institute. Currently 5 models from 4 papers have been reimplemented in PyTorch, namely Two-Stream CNN (ICMEW'17), HCN (IJCAI'18), HCN-Baseline (AAAI'22), Ta-CNN (AAAI'22) and Dynamic GCN (ACM MM'20).

Installation

SkelAct is based on MMAction2. Follow the instruction below to setup a valid Python environment.

  • Linux (CUDA)
conda create -n skelact python=3.9 -y
conda activate skelact
conda install pytorch=1.11.0 torchvision=0.12.0 cudatoolkit=11.3 -c pytorch -y
pip install 'mmcv-full==1.5.0' -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.11.0/index.html
pip install mmaction2  # tested mmaction2 v0.24.0
  • macOS (CPU only)
conda create -n skelact python=3.9 -y
conda activate skelact
conda install pytorch=1.12.0 torchvision=0.13.0 -c pytorch -y
pip install 'mmcv-full==1.5.0'
git clone --depth 1 --branch v0.24.0 https://github.com/open-mmlab/mmaction2.git
cd mmaction2
sed -i '' '/decord/d' requirements/build.txt  # remove decord from requirements
CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install .

Get Started

Data Preparation

Use gen_ntu_rgbd_raw.py to preprocess the NTU RGB+D dataset. Put the dataset in data/ with the following structure.

data/
└── ntu
    └── nturgb+d_skeletons_60_3d
        ├── xsub
        │   ├── train.pkl
        │   └── val.pkl
        └── xview
            ├── train.pkl
            └── val.pkl

Train

You can use the following command to train a model.

./tools/run.sh ${CONFIG_FILE} ${GPU_IDS} ${SEED}

Example: train HCN model on the joint data of NTU RGB+D using 2 GPUs with seed 0.

./tools/run.sh configs/hcn/hcn_ntu60_xsub_joint.py 0,1 0

Test

You can use the following command to test a model.

python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [optional arguments]

Example: test HCN model on the joint data of NTU RGB+D.

python tools/test.py configs/hcn/hcn_ntu60_xsub_joint.py \
    work_dirs/hcn_ntu60_xsub_joint/best_top1_acc_epoch_475.pth \
    --eval top_k_accuracy --cfg-options "gpu_ids=[0]"

Models

FLOPs and Params

Model GFLOPs1 Params (M)
Two-Stream CNN 0.098 0.785
HCN 0.196 1.047
HCN-Baseline 0.196 0.538
Ta-CNN 0.147 0.532
Dynamic GCN 2.395 3.75

1 Calculated with get_flops.py, which may differ from the numbers reported in the papers.

Performance

All the following models are trained using 2 TITAN X Pascal GPUs. Note that for simplicity we do not strictly follow the details (e.g. data preprocessing) of the original implementations, which causes the slight accuracy difference.

  • NTU RGB+D XSub
Model Config Our Acc (5 seeds2) Our Acc (mean±std) Paper Acc
Two-Stream CNN tscnn_ntu60_xsub_joint.py 83.93, 83.78, 83.56, 84.04, 84.13 83.89±0.20 83.2
HCN hcn_ntu60_xsub_joint.py 86.69, 85.89, 86.17, 86.63, 87.09 86.49±0.42 86.5
HCN-Baseline hcnb_ntu60_xsub_joint.py 87.89, 87.26, 87.71, 87.72, 87.77 87.67±0.21 87.4
Ta-CNN tacnn_ntu60_xsub_joint.py 88.65, 88.53, 88.76, 88.49, 88.21 88.53±0.19 88.8
Dynamic GCN dgcn_65e_ntu60_xsub_joint.py 88.49, 89.03, 88.87, 88.88, 89.23 88.90±0.24 89.2

2 Seed = {0, 1, 2, 3, 4}

  • NTU RGB+D XView
Model Config Our Acc3 Paper Acc
Two-Stream CNN tscnn_ntu60_xview_joint.py 90.23 89.3
HCN hcn_ntu60_xview_joint.py 92.35 91.1
Ta-CNN tacnn_ntu60_xview_joint.py 93.91 -
Dynamic GCN dgcn_65e_ntu60_xview_joint.py 94.23 -

3 Seed = 0

License

This project is released under the Apache 2.0 license.

Citation

@inproceedings{li2017skeleton,
  title={Skeleton-based Action Recognition with Convolutional Neural Networks},
  author={Li, Chao and Zhong, Qiaoyong and Xie, Di and Pu, Shiliang},
  booktitle={2017 IEEE International Conference on Multimedia \& Expo Workshops},
  pages={597--600},
  year={2017}
}

@inproceedings{li2018co-occurrence,
  title={Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation},
  author={Li, Chao and Zhong, Qiaoyong and Xie, Di and Pu, Shiliang},
  booktitle={Proceedings of the 27th International Joint Conference on Artificial Intelligence},
  pages={786--792},
  year={2018}
}

@inproceedings{ye2020dynamic,
  title={Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action Recognition},
  author={Ye, Fanfan and Pu, Shiliang and Zhong, Qiaoyong and Li, Chao and Xie, Di and Tang, Huiming},
  booktitle={Proceedings of the 28th ACM International Conference on Multimedia},
  pages={55--63},
  year={2020}
}

@inproceedings{xu2022topology,
  title={Topology-aware Convolutional Neural Network for Efficient Skeleton-based Action Recognition},
  author={Xu, Kailin and Ye, Fanfan and Zhong, Qiaoyong and Xie, Di},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}

Acknowledgement

SkelAct heavily depends on MMAction2. We appreciate all contributors to the excellent framework.