TPAMI 2021 ReID-Survey with a Powerful AGW Baseline

Deep Learning for Person Re-identification: A Survey and Outlook. PDF with supplementary materials. arXiv

• An implementation of AGW for cross-modality visible-infrared Re-ID is HERE.

• An implementation of AGW for video Re-ID is HERE

• An implementation of AGW for partial Re-ID is HERE.

A simplified introduction in Chinese on 知乎.

Highlights

• A comprehensive survey with in-depth analysis for closed- and open-world person Re-ID in recent years (2016-2020).

• A new evaluation metric, namely mean Inverse Negative Penalty (mINP), which measures the ability to find the hardest correct match.

• A new AGW baseline with non-local Attention block, Generalized mean pooling and Weighted regularization triplet. It acheieves competitive performance on FOUR challenging Re-ID tasks, including single-modality image-based Re-ID, video-based Re-ID, Partial Re-ID and cross-modality Re-ID.

AGW on Single-Modality Image Re-ID with mINP

DukeMTMC dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	86.4%	76.4%	40.7%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. PDF
ABD-Net	ImageNet	89.0%	78.6%	42.1%	Code	ABD-Net: Attentive but Diverse Person Re-Identification. In ICCV 19. PDF
AGW	ImageNet	89.0%	79.6%	45.7%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook

Market-1501 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	94.5%	85.9%	59.4%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. arXiv
ABD-Net	ImageNet	95.6%	88.3%	66.2%	Code	ABD-Net: Attentive but Diverse Person Re-Identification. In ICCV 19. PDF
AGW	ImageNet	95.1%	87.8%	65.0%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

CUHK03 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	58.0%	56.6%	43.8%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. PDF
AGW	ImageNet	63.6%	62.0%	50.3%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

MSMT17 dataset

Method	Pretrained	Rank@1	mAP	mINP	Model	Paper
BagTricks	ImageNet	63.4%	45.1%	12.4%	Code	Bag of Tricks and A Strong Baseline for Deep Person Re-identification. In ArXiv 19. arXiv
AGW	ImageNet	68.3%	49.3%	14.7%	GoogleDrive	Deep Learning for Person Re-identification: A Survey and Outlook. In ArXiv 20. arXiv

Quick Start

1. Prepare dataset

Create a directory to store reid datasets under this repo, taking Market1501 for example

cd ReID-Survey
mkdir toDataset

• Set _C.DATASETS.ROOT_DIR = ('./toDataset') inconfig/defaults.py

• Download dataset to toDataset/ from http://www.liangzheng.org/Project/project_reid.html

• Extract dataset and rename to market1501. The data structure would like:

toDataset
    market1501 
        bounding_box_test/
        bounding_box_train/
        ......

Partial-REID and Partial-iLIDS datasets are provided by https://github.com/lingxiao-he/Partial-Person-ReID

2. Install dependencies

• pytorch=1.0.0
• torchvision=0.2.1
• pytorch-ignite=0.1.2
• yacs
• scipy=1.2.1
• h5py

3. Train

To train a AGW model with on Market1501 with GPU device 0, run similarly:

python3 tools/main.py --config_file='configs/AGW_baseline.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')" OUTPUT_DIR "('./log/market1501/Experiment-AGW-baseline')"

4. Test

To test a AGW model with on Market1501 with weight file './pretrained/dukemtmc_AGW.pth', run similarly:

python3 tools/main.py --config_file='configs/AGW_baseline.yml' MODEL.DEVICE_ID "('0')" DATASETS.NAMES "('market1501')"  MODEL.PRETRAIN_CHOICE "('self')" TEST.WEIGHT "('./pretrained/market1501_AGW.pth')" TEST.EVALUATE_ONLY "('on')" OUTPUT_DIR "('./log/Test')"

Citation

Please kindly cite this paper in your publications if it helps your research:

@article{pami21reidsurvey,
  title={Deep Learning for Person Re-identification: A Survey and Outlook},
  author={Ye, Mang and Shen, Jianbing and Lin, Gaojie and Xiang, Tao and Shao, Ling and Hoi, Steven C. H.},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2021},
}

Contact: mangye16@gmail.com