• NTU RGB+D: Cross View (CV), Cross Subject (CS)
• Kinect-skeleton

• Action recognition

• Visdom supported.

Our code is based on Python3.5. There are a few dependencies to run the code in the following:

• Python (>=3.5)
• PyTorch (0.4.0)
• torchnet
• Visdom
• Other version info about some Python packages can be found in requirements.txt

To transform raw NTU RGB+D data into numpy array (memmap format ) by this command:

python ./feeder/ntu_gendata.py --data_path <path for raw skeleton dataset> --out_folder <path for new dataset>

To generate the bone information:

python ./feeder/ntu_gen_bone_data.py --data_path <path for raw skeleton dataset> --out_folder <path for new dataset>

To generate the core information:

python ./feeder/ntu_gen_core_data.py --data_path <path for raw skeleton dataset> --out_folder <path for new dataset>

Not supported now.

Before you start the training, you have to launch visdom server.

python -m visdom

To train the model, you should note that:

• --dataset_dir is the parents path for all the datasets,
• --num the number of experiments trials (type: list).

python main.py --dataset_dir <parents path for all the datasets> --mode train --model_name AIF_CNN --dataset_name NTU-RGB-D-CV --num 01

To run a new trial with different parameters, you need to:

• Firstly, run the above training command with a new trial number, e.g, --num 03, thus you will got an error.
• Secondly, copy a parameters file from the ./experiments/NTU-RGB-D-CV/AIF_CNN01/params.json to the path of your new trial "./experiments/NTU-RGB-D-CV/AIF_CNN03/params.json" and modify the information(data, bone or core) as you want.
• At last, run the above training command again, it will works.

python main.py --dataset_dir <parents path for all the datasets> --mode test --load True --model_name AIF_CNN --dataset_name NTU-RGB-D-CV --num 01

You also can load a half trained model, and start training it from a specific checkpoint by the following command:

python main.py --dataset_dir <parents path for all the datasets> --mode load_train --load True --model_name AIF_CNN --dataset_name NTU-RGB-D-CV --num 01 --load_model <path for  trained model>

python main.py --dataset_dir <parents path for all the datasets> --mode test --load True --model_name AIF_CNN --dataset_name NTU-RGB-D-CV --num 01

Combine the generated scores with:

python merge_for_cs.py/merge_for_cv.py

[1] Chao Li, Qiaoyong Zhong, Di Xie, Shiliang Pu. Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation. IJCAI 2018. http://arxiv.org/pdf/1804.06055.pdf

[2] huguyuehuhu/HCN-pytorch.

[3] kenziyuliu/Unofficial-DGNN-PyTorch.