Skip to content
master
Switch branches/tags
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
log
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Feel free to visit my homepage and awesome person re-id github page


Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification [CVPR2020 paper]


Presentation video

1-minute version (ENG)

Video Label

5-minute version (KOR)

Video Label


<Illustration of our Hierarchical Cross-Modality Disentanglement (Hi-CMD) concept>

Prerequisites

  • Ubuntu 18.04
  • Python 3.6
  • PyTorch 1.0+ (recent version is recommended)
  • NVIDIA GPU (>= 8.5GB)
  • CUDA 10.0 (optional)
  • CUDNN 7.5 (optional)

Getting Started

Installation

  • Configure virtual (anaconda) environment
conda create -n env_name python=3.6
source activate env_name
conda install pytorch torchvision cudatoolkit=10.0 -c pytorch
  • Install python libraries
conda install -c conda-forge matplotlib
conda install -c anaconda yaml
conda install -c anaconda pyyaml 
conda install -c anaconda scipy
conda install -c anaconda pandas 
conda install -c anaconda scikit-learn 
conda install -c conda-forge opencv
conda install -c anaconda seaborn
conda install -c conda-forge tqdm
git clone https://github.com/Cadene/pretrained-models.pytorch.git
cd pretrained-models.pytorch
python setup.py install

Training and testing

  • In the actual experiment, a total of 10 experiment sets are used.
  • Here is a simple example of running an experiment on only one set (RegDB-01).
  • Download [RegDB_01] (for a simple test)
    • The RegDB_01 dataset should be included in './data/'
    • Ex: ./HiCMD/data/RegDB_01/
  • You can download the entire sets of RegDB at this [link]
    • If you will use this dataset, please refer to the papers [1,2] below.

Training

sh train.sh

Testing on pretrained model

1) RegDB_01 [1,2]

  • Download RegDB_pretrained
    • The pretrained RegDB_01 model should be included in './pretrained/'
    • Ex: ./HiCMD/pretrained/checkpoints/
sh test.sh
  • The code provides the following results.
Metric Value
Rank1 70.44%
Rank5 79.37%
Rank10 85.15%
Rank20 91.55%
mAP 65.93%
  • Note that the performance of the manuscript (Rank1: 70.93%) is obtained by averaging this experiment for all 10 sets.
  • If the code is not working, please refer to './pretrained/test_results/net_70000_RegDB_01_(ms1.0)_f1_test_result.txt'

2) SYSU-MM01 [3]

  • MATLAB is required for evaluating SYSU-MM01 (official code).

  • Download SYSU_features

    • The pretrained SYSU-MM01 features should be included in './eval_SYSU/'
    • Ex: ./HiCMD/eval_SYSU/
  • The code provides the following results.

Metric Value
Rank1 34.94%
Rank5 65.48%
Rank10 77.58%
Rank20 88.38%
mAP 35.94%
  • If the code is not working, please refer to './eval_SYSU/results_test_SYSU.txt'

(Optional) If all the files can not downloaded in the above links, please check the below links.

(Optional) Additional experiments

  • If you want to experiment with all sets of RegDB, download the entire dataset:

    • The RegDB dataset [1, 2] can be downloaded from this link. (The original name is "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)").
    • If you will use this dataset, please refer to the papers [1,2] below.
  • If you want to experiment with SYSU-MM01, download the official dataset:

    • The SYSU-MM01 dataset [3] can be downloaded from this website.
    • The authors' official matlab code is used to evaluate the SYSU dataset.
    • If you will use this dataset, please refer to the paper [3] below.
  • Change the 'data_name' from 'RegDB_01' to the name of other datasets.

  • Process the downloaded data according to the code by python prepare.py.

  • Train and test

Acknowledgement

The code is based on the PyTorch implementation of the Person_reID_baseline_pytorch, Cross-Model-Re-ID-baseline, MUNIT, DGNET, SYSU-evaluation.

Citation

@InProceedings{Choi_2020_CVPR,
author = {Choi, Seokeon and Lee, Sumin and Kim, Youngeun and Kim, Taekyung and Kim, Changick},
title = {Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}

Reference

  • [1] D. T. Nguyen, H. G. Hong, K. W. Kim, and K. R. Park. Person recognition system based on a combination of body images from visible light and thermal cameras. Sensors, 17(3):605, 2017.

  • [2] Jin Kyu Kang, Toan Minh Hoang, and Kang Ryoung Park, “Person Re-Identification Between Visible and Thermal Camera Images Based on Deep Residual CNN Using Single Input,” IEEE Access, Vol. 7, pp. 57972-57984, May 2019

  • [3] A. Wu, W.-s. Zheng, H.-X. Yu, S. Gong, and J. Lai. Rgb-infrared crossmodality person re-identification. In IEEE International Conference on Computer Vision (ICCV), pages 5380–5389, 2017.