A. Sedighi Moghaddam, F. Anvari, MJ. Mirshekari, MA. Fakhari, MR. Mohammadi
Computer Vision Lab of Iran University of Science and Technology - CVLab IUSTThe IUST_PersonReID dataset was developed to address limitations in existing person re-identification datasets by including cultural and environmental contexts unique to Islamic countries, especially Iran and Iraq. Unlike common datasets, which don’t reflect the clothing styles common in these regions—such as hijabs and other coverings—the IUST_PersonReID dataset represents this diversity, helping to reduce demographic bias and improve model accuracy. Collected from a variety of real-world settings under different lighting, camera angles, indoor & outdoor, and weather conditions, this dataset provides extensive, overlapping views across multiple cameras. By capturing these unique conditions, IUST_PersonReID offers a valuable resource for developing re-ID models that perform more reliably across diverse environments and populations.
@article{moghaddam2024iust_personreid,
title = {IUST_PersonReId: A New Domain in Person Re-Identification Datasets},
author = {Moghaddam, Alireza Sedighi and Anvari, Fatemeh and Haghighi, Mohammadjavad Mirshekari and Fakhari, Mohammadali and Mohammadi, Mohammad Reza},
journal = {arXiv preprint arXiv:2412.18874},
year = {2024}
}