Awesome

ReID_extra_testdata

Sample Input

This repository stores the example input of the ReID sample in https://github.com/ReID-Team/opencv/tree/sample_person_reid

Baseline Model

Following link store several state-of-the-art baseline models from Tencent Youtu Lab.

Download Link

models

Model Performance

Model	Market1501(mAP/rank-1)	DukeMTMC(mAP/rank-1)	MSMT17(mAP/rank-1)
youtu_reid_baseline_lite	87.86/95.01	79.75/89.05	58.82/80.81
youtu_reid_baseline_medium	90.75/96.32	83.38/91.56	65.30/85.08
youtu_reid_baseline_large	91.85/96.73	84.40/91.88	68.68/87.04

Reference

Following datasets are used for the baseline training:

Market1501

@inproceedings{zheng2015scalable,
  title={Scalable person re-identification: A benchmark},
  author={Zheng, Liang and Shen, Liyue and Tian, Lu and Wang, Shengjin and Wang, Jingdong and Tian, Qi},
  booktitle={Proceedings of the IEEE international conference on computer vision},
  pages={1116--1124},
  year={2015}
}

DukeMTMC

@inproceedings{ristani2016performance,
  title={Performance measures and a data set for multi-target, multi-camera tracking},
  author={Ristani, Ergys and Solera, Francesco and Zou, Roger and Cucchiara, Rita and Tomasi, Carlo},
  booktitle={European Conference on Computer Vision},
  pages={17--35},
  year={2016},
  organization={Springer}
}

CHUK03

@inproceedings{li2014deepreid,
  title={DeepReID: Deep Filter Pairing Neural Network for Person Re-identification},
  author={Li, Wei and Zhao, Rui and Xiao, Tong and Wang, Xiaogang},
  booktitle={CVPR},
  year={2014}
}

MSMT17

@inproceedings{wei2018person,
  title={Person transfer gan to bridge domain gap for person re-identification},
  author={Wei, Longhui and Zhang, Shiliang and Gao, Wen and Tian, Qi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={79--88},
  year={2018}
}