Deep Spatial Feature Reconstruction for Partial Person Re-identification

Lingxiao He, Xingyu Liao

[CVPR2018] [BibTeX]

Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification

Lingxiao He, Xingyu Liao

[ICCV2019] [BibTeX]

First install FastReID, and then put Partial Datasets in directory datasets. The whole framework of FastReID-DSR is

and the detail you can refer to

The datasets can find in Google Drive

PartialREID---gallery: 300 images of 60 ids, query: 300 images of 60 ids

PartialiLIDS---gallery: 119 images of 119 ids, query: 119 images of 119 ids

OccludedREID---gallery: 1,000 images of 200 ids, query: 1,000 images of 200 ids

To train a model, run:

python3 projects/PartialReID/train_net.py --config-file <config.yaml>

For example, to train the re-id network with IBN-ResNet-50 Backbone one should execute:

CUDA_VISIBLE_DEVICES='0,1,2,3' python3 projects/PartialReID/train_net.py --config-file 'projects/PartialReID/configs/partial_market.yml'

If you use DSR or FPR, please use the following BibTeX entry.

@inproceedings{he2018deep,
  title={Deep spatial feature reconstruction for partial person re-identification: Alignment-free approach},
  author={He, Lingxiao and Liang, Jian and Li, Haiqing and Sun, Zhenan},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2018}
}
@inproceedings{he2019foreground,
  title={Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification},
  author={He, Lingxiao and Wang, Yinggang and Liu, Wu and Zhao, He and Sun, Zhenan and Feng, Jiashi},
  booktitle={IEEE International Conference on Computer Vision (ICCV)},
  year={2019}
}