liujf69/IPP-Net-Parsing

[CICAI 2023] Implementation of the paper “Integrating Human Parsing and Pose Network for Human Action Recognition”.

IPP-Net_Parsing

This is the official repo of IPP-Net and our work Integrating Human Parsing and Pose Network for Human Action Recognition is accepted by CAAI International Conference on Artificial Intelligence (CICAI 2023).

Prerequisites

You can install necessary dependencies by running pip install -r requirements.txt
Then, you need to install torchlight by running pip install -e torchlight

Data Preparation

Download datasets:

1. NTU RGB+D 60 Skeleton dataset from https://rose1.ntu.edu.sg/dataset/actionRecognition/
   
2. NTU RGB+D 120 Skeleton dataset from https://rose1.ntu.edu.sg/dataset/actionRecognition/
   
3. NTU RGB+D 60 Video dataset from https://rose1.ntu.edu.sg/dataset/actionRecognition/
   
4. NTU RGB+D 120 Video dataset from https://rose1.ntu.edu.sg/dataset/actionRecognition/
   
5. Put downloaded skeleton data into the following directory structure:

- data/
  - ntu/
  - ntu120/
  - nturgbd_raw/
    - nturgb+d_skeletons
        S001C001P001R001A001.skeleton
        ...
    - nturgb+d_skeletons120/
        S018C001P008R001A061.skeleton
        ...

6. Extract person frames from the video dataset according to the following project: Extract_NTU_Person
   

Process skeleton data

cd ./data/ntu or cd ./data/ntu120
python get_raw_skes_data.py
python get_raw_denoised_data.py
python seq_transformation.py

Extract human parsing data

1. cd ./Human_parsing
2. Download checkpoints and put it into the ./checkpoints/resnet101 folder: PSP_Net
   

Run:

python gen_parsing.py --samples_txt_path ./ntu120.txt \
      --ntu60_path person_frame_path \
      --ntu120_path person_frame_path

Example:

python gen_parsing.py --samples_txt_path ./test_sample.txt \
      --ntu60_path ./dataset/ntu60/ \
      --ntu120_path ./dataset/ntu120/

Training pose branch

Training NTU60

On the benchmark of XView, using joint modality, run: python Pose_main.py --device 0 1 --config ./config/nturgbd-cross-view/joint.yaml
On the benchmark of XSub, using joint modality, run: python Pose_main.py --device 0 1 --config ./config/nturgbd-cross-subject/joint.yaml

Training NTU120

On the benchmark of XSub, using joint modality, run: python Pose_main.py --device 0 1 --config ./config/nturgbd120-cross-subject/joint.yaml
On the benchmark of XSet, using joint modality, run: python Pose_main.py --device 0 1 --config ./config/nturgbd120-cross-set/joint.yaml

Training parsing branch

Training NTU60

On the benchmark of XView, run: python Parsing_main.py recognition -c ./config/nturgbd-cross-view/parsing_train.yaml
On the benchmark of XSub, run: python Parsing_main.py recognition -c ./config/nturgbd-cross-subject/parsing_train.yaml

Training NTU120

On the benchmark of XSub, run: python Parsing_main.py recognition -c ./config/nturgbd120-cross-subject/parsing_train.yaml
On the benchmark of XSet, run: python Parsing_main.py recognition -c ./config/nturgbd120-cross-set/parsing_train.yaml

Testing

Testing NTU120XSub

python ensemble.py --benchmark NTU120XSub --joint_Score ./Pose/ntu120_XSub_joint.pkl --bone_Score ./Pose/ntu120_XSub_bone.pkl --jointmotion_Score ./Pose/ntu120_XSub_jointmotion.pkl --bonemotion_Score ./Pose/ntu120_XSub_bonemotion.pkl --parsing_Score ./Parsing/ntu120_XSub_best.pkl --val_sample ./Val_sample/NTU120_CTR_CSub_test.txt --match_txt ./Match_txt/ntu120_XSubpair.txt

Testing NTU120XSet

python ensemble.py --benchmark NTU120XSet --joint_Score ./Pose/ntu120_XSet_joint.pkl --bone_Score ./Pose/ntu120_XSet_bone.pkl --jointmotion_Score ./Pose/ntu120_XSet_jointmotion.pkl --bonemotion_Score ./Pose/ntu120_XSet_bonemotion.pkl --parsing_Score ./Parsing/ntu120_XSet_best.pkl --val_sample ./Val_sample/NTU120_CTR_CSet_test.txt --match_txt ./Match_txt/ntu120_XSetpair.txt

Testing NTU60XSub

python ensemble.py --benchmark NTU60XSub --joint_Score ./Pose/ntu60_XSub_joint.pkl --bone_Score ./Pose/ntu60_XSub_bone.pkl --jointmotion_Score ./Pose/ntu60_XSub_jointmotion.pkl --bonemotion_Score ./Pose/ntu60_XSub_bonemotion.pkl --parsing_Score ./Parsing/ntu60_XSub_best.pkl --val_sample ./Val_sample/NTU60_CTR_CSub_test.txt --match_txt ./Match_txt/ntu60_XSubpair.txt

Testing NTU60XView

python ensemble.py --benchmark NTU60XView --joint_Score ./Pose/ntu60_XView_joint.pkl --bone_Score ./Pose/ntu60_XView_bone.pkl --jointmotion_Score ./Pose/ntu60_XView_jointmotion.pkl --bonemotion_Score ./Pose/ntu60_XView_bonemotion.pkl --parsing_Score ./Parsing/ntu60_XView_best.pkl --val_sample ./Val_sample/NTU60_CTR_CView_test.txt --match_txt ./Match_txt/ntu60_XViewpair.txt

Citation

@inproceedings{ding2023integrating,
  author={Ding, Runwei and Wen, Yuhang and Liu, Jinfu and Dai, Nan and Meng, Fanyang and Liu, Mengyuan},
  title={Integrating Human Parsing and Pose Network for Human Action Recognition}, 
  booktitle={Proceedings of the CAAI International Conference on Artificial Intelligence (CICAI)}, 
  year={2023}
}

Contact

For any questions, feel free to contact: liujf69@mail2.sysu.edu.cn