VPULab-CityFlow-ReID-Evaluation-Framework

Paper

Python evaluation framework described in our paper (https://link.springer.com/article/10.1007/s11042-023-14511-0)

Setup

Requirements

The repository has been tested in the following software.

• Ubuntu 18.04
• Python 3.7
• Anaconda

Clone repository

$ git clone https://github.com/Paulamde/VPULab-CityFlow-ReID-Evaluation-Framework.git

Anaconda environment

To create and setup the Anaconda Enviroment, open a terminal window in the repository folder and type:

$ conda env create -f envEvCity.yml
$ conda activate EvCity

Running

Dataset

This github provides the Ground truth annotations of the CityFlow-ReID test set. The entire dataset can be downloaded at https://www.aicitychallenge.org/2020-data-and-evaluation/.

Run evaluation code

To run the evaluation code you need your track2.txt file obtained with your ReID system. To test the code, we provide a sample track2.txt

$ python Evaluation_CityFlow.py  

Citation

If you find this code and work useful, please consider citing:

@article{moral2023enhancing,
  title={Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information},
  author={Moral, Paula and Garc{\'\i}a-Mart{\'\i}n, {\'A}lvaro and Mart{\'\i}nez, Jos{\'e} M and Besc{\'o}s, Jes{\'u}s},
  journal={Multimedia Tools and Applications},
  pages={1--21},
  year={2023},
  publisher={Springer}
}

Acknowledgment

This work is part of the preliminary tasks related to the Harvesting Visual Data (HVD) project (PID2021-125051OB-I00) funded by the Ministerio de Ciencia e Innovación of the Spanish Government.