Deep Spatial Feature Reconstruction for Partial Person Re-identification
Lingxiao He, Xingyu Liao
Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification
Lingxiao He, Xingyu Liao
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'| Method | PartialREID | OccludedREID | PartialiLIDS |
|---|---|---|---|
| Rank@1 (mAP) | Rank@1 (mAP) | Rank@1 (mAP) | |
| DSR (CVPR’18) | 73.7(68.1) | 72.8(62.8) | 64.3(58.1) |
| FPR (ICCV'19) | 81.0(76.6) | 78.3(68.0) | 68.1(61.8) |
| FastReID-DSR | 82.7(76.8) | 81.6(70.9) | 73.1(79.8) |
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}
}