Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization [link]
The implementation code and models of TASTR.
Environment
- Ubuntu 16.04
- Python 3.7
- Pytorch 1.0.1
- CUDA 9.0
- NVIDIA GTX 1080ti x 2
Installation
git clone https://github.com/xieqk/TASTR.git
Prepare dataset
Download DukeMTMC-reID dataset then extract file to your data directory ( denoted as ${data_root}
). The file structure is as follows
${data_root}
└── dukemtmc-reid/
└── DukeMTMC-reID
├── bounding_box_test
├── bounding_box_train
├── CITATION.txt
├── LICENSE_DukeMTMC-reID.txt
├── LICENSE_DukeMTMC.txt
├── query
└── README.md
Training
modify the ${data_root}
in ./scripts/train_dukemtmcreid.sh
, then run
./scripts/train_dukemtmcreid.sh
Citation
@article{xie2020progressive,
title={Progressive Unsupervised Person Re-identification by Tracklet Association with Spatio-Temporal Regularization},
author={Xie, Qiaokang and Zhou, Wengang and Qi, Guo-Jun and Tian, Qi and Li, Houqiang},
journal={IEEE Transactions on Multimedia (TMM)},
year={2020},
publisher={IEEE}
}
Request the Campus4K dataset from xieqiaok [at] mail.ustc.edu.cn.