Exploiting Robust Unsupervised Video Person Re-identification
Implementation of the proposed uPMnet. For the preprint, please refer to [Arxiv].
Getting Started
Requirements
Here is a brief instruction for installing the experimental environment.
# install virtual envs
$ conda create -n uPMnet python=2.7 -y
$ conda activate uPMnet
# install tensorflow 1.4.0 with cuda 9.0
$ pip install --ignore-installed --upgrade https://github.com/mind/wheels/releases/download/tf1.4-gpu-cuda9/tensorflow-1.4.0-cp27-cp27mu-linux_x86_64.whl
# install mkl
$ sudo apt install cmake
$ git clone --branch v0.12 https://github.com/01org/mkl-dnn.git
$ cd mkl-dnn/scripts; ./prepare_mkl.sh && cd ..
$ mkdir -p build && cd build && cmake .. && make -j36
$ sudo make install
$ echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib' >> ~/.bashrc
# install other dependencies
$ pip install scipy matplotlib
Convert benchmarks to tfrecords
# Please modify the path in your way
$ bash datasets/convert_data_to_tfrecords.py
Download pre-trained models
The Mobilenet and Resnet models can be downloaded in this link (code: 1upx) and should be put in the checkpoints
directory.
Training and Extracting features
$ bash scripts/train_PRID2011.sh # train_iLIDS_VID.sh or train_DukeMTMC-VideoReID.sh
Testing
Use the Matlab to run the following files, evaluation/CMC_PRID2011.m
, evaluation/CMC_iLIDS-VID.m
, and evaluation/CMC_DukeMTMC_VideoReID.m
.
Results in the Paper
The results of PRID2011, iLIDS-VID, and DukeMTMC-VideoReID are provided.
Model | Rank-1@PRID2011 | Rank-1@iLIDS-VID | Rank-1@DukeMTMC-VideoReID |
---|---|---|---|
uPMnet | 92.0 link (code: xa7z) | 63.1 link (code: le2c) | 83.6 link (code: e9ja) |
You can download these results and put them in the results
directory. Then use Matlab to evaluate them.
Acknowledgement
This repository is built upon the repository DAL.
Citation
If you find this project useful for your research, please kindly cite:
@article{zang2021exploiting,
title={Exploiting Robust Unsupervised Video Person Re-identification},
author={Zang, Xianghao and Li, Ge and Gao, Wei and Shu, Xiujun},
journal={arXiv preprint arXiv:2111.05170},
year={2021}
}
License
This repository is released under the GPL-2.0 License as found in the LICENSE file.