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[AAAI2022] This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

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WangTaoAs/PFD_Net

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PFD:Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer

Python >=3.6 PyTorch >=1.6

This repo is the official implementation of "Pose-guided Feature Disentangling for Occluded Person Re-identification based on Transformer(PFD), Tao Wang, Hong Liu, Pinhao Song, Tianyu Guo& Wei Shi" in PyTorch.PFD-Net

Pipeline

framework

Dependencies

  • timm==0.3.2

  • torch==1.6.0

  • numpy==1.20.2

  • yacs==0.1.8

  • opencv_python==4.5.2.54

  • torchvision==0.7.0

  • Pillow==8.4.0

Installation

pip install -r requirements.txt

If you find some packages are missing, please install them manually.

Prepare Datasets

mkdir data

Please download the dataset, and then rename and unzip them under the data

data
|--market1501
|
|--Occluded_Duke
|
|--Occluded_REID
|
|--MSMT17
|
|--dukemtmcreid

Prepare ViT Pre-trained and HRNet Pre-trained Models

mkdir data

The ViT Pre-trained model can be found in ViT_Base, The HRNet Pre-trained model can be found in HRNet, please download it and put in the './weights' dictory.

Training

We use One GeForce GTX 1080Ti GPU for Training Before train the model, please modify the parameters in config file, please refer to Arguments in TransReID

python occ_train.py --config_file {config_file path}
#examples
1. For Occluded-Duke:
python occ_train.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'
2. For Market-1501:
python occ_train.py --config_file 'configs/market1501/skeleton_pfd.yml'
3. For DUKEMTMC:
python occ_train.py --config_file 'configs/dukemtmcreid/skeleton_pfd.yml'
......

Test the model

First download the Occluded-Duke model:Occluded-Duke

To test on pretrained model on Occ-Duke: Modify the pre-trained model path (PRETRAIN_PATH:ViT_Base, POSE_WEIGHT:HRNet, WEIGHT:Occluded-Duke) in yml, and then run:

## OccDuke for example
python test.py --config_file 'configs/OCC_Duke/skeleton_pfd.yml'

Occluded-Duke Results

Model Image Size Rank-1 mAP
HOReID 256*128 55.1 43.8
PAT 256*128 64.5 53.6
TransReID 256*128 64.2 55.7
PFD 256*128 67.7 60.1
TransReID* 256*128 66.4 59.2
PFD* 256*128 69.5 61.8

$*$means the encoder is with a small step sliding-window setting

Occluded-REID Results

Model Image Size Rank-1 mAP
HOReID 256*128 80.3 70.2
PAT 256*128 81.6 72.1
PFD 256*128 79.8 81.3

Market-1501 Results

Model Image Size Rank-1 mAP
HOReID 256*128 80.3 70.2
PAT 256*128 95.4 88.0
TransReID 256*128 95.4 88.0
PFD 256*128 95.5 89.6

DukeMTMC Results

Model Image Size Rank-1 mAP
HOReID 256*128 86.9 75.6
PAT 256*128 88.8 78.2
TransReID 256*128 89.6 80.6
PFD 256*128 90.6 82.2
TransReID* 256*128 90.7 82.0
PFD* 256*128 91.2 83.2

Citation

If you find our work useful in your research, please consider citing this paper! (preprint version will be available soon)

Arxiv:

@misc{wang2021poseguided,
      title={Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer}, 
      author={Tao Wang and Hong Liu and Pinhao Song and Tianyu Guo and Wei Shi},
      year={2021},
      eprint={2112.02466},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

AAAI:

@inproceedings{wang2022pose,
  title={Pose-guided feature disentangling for occluded person re-identification based on transformer},
  author={Wang, Tao and Liu, Hong and Song, Pinhao and Guo, Tianyu and Shi, Wei},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={3},
  pages={2540--2549},
  year={2022}
}

Acknowledgement

Our code is extended from the following repositories. We thank the authors for releasing the codes.

License

This project is licensed under the terms of the MIT license.

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[AAAI2022] This is Official implementation for "Pose-guided Feature Disentangling for Occluded Person Re-Identification Based on Transformer" in AAAI2022

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