Implementation of the proposed uPMnet. For the preprint, please refer to [Arxiv].
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
# Please modify the path in your way
$ bash datasets/convert_data_to_tfrecords.py
The Mobilenet and Resnet models can be downloaded in this link (code: 1upx) and should be put in the checkpoints directory.
$ bash scripts/train_PRID2011.sh # train_iLIDS_VID.sh or train_DukeMTMC-VideoReID.sh
Use the Matlab to run the following files, evaluation/CMC_PRID2011.m, evaluation/CMC_iLIDS-VID.m, and evaluation/CMC_DukeMTMC_VideoReID.m.
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.
This repository is built upon the repository DAL.
If you find this project useful for your research, please kindly cite:
@article{zang2021exploiting,
author = {Xianghao Zang and Ge Li and Wei Gao and Xiujun Shu},
title = {Exploiting robust unsupervised video person re‐identification},
journal = {IET Image Processing},
volume = {16},
number = {3},
pages = {729-741},
year = {2022},
doi = {10.1049/ipr2.12380}
}
This repository is released under the GPL-2.0 License as found in the LICENSE file.
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