Key Point-aware Occlusion Suppression and Semantic Alignment for Occluded Person Re-identification

• Introduction: We proposed a Key-Point-aware Occlusion suppression and Semantic alignment (POS) method for occluded person re-ID.

Properties

• Dataset: Support multiple Datasets Please obtain the data set according to PGFA
• Key Points Detection: You can download the Hrnet pertrain model from here for pose estimation.

Requirements:

CUDA  10.2
Python  3.8
Pytorch  1.6.0
torchvision  0.2.2
numpy  1.19.0

Train and Test

Train on Occluded DukeMTMC/Market-1501/DukeMTMC-reID

python3 main.py --mode train \
    --train_dataset your_train_dataset_name --test_dataset your_test_dataset_name \
    --market(or_duke_or_occduke)_path /path/to/market/dataset/ \
    --output_path ./results/your_dataset_name

python3 main.py --mode test \
    --train_dataset your_train_dataset_name --test_dataset your_test_dataset_name \
    --market(or_duke_or_occduke)_path /path/to/market/dataset/ \
    --output_path ./results/your_dataset_name

Cite

The corresponding papers will be published in Information Science，2022.

If you have any questions, please contact us with 1216246628@qq.com