Social Distancing Monitoring by Human Detection Through Bird’s-Eye View Technique

Gona Rozhbayani, Amel Tuama, Fadwa Al-Azzo

2024

Abstract

The objective of this study is to offer a YOLOv5 deep learning-based system for social distance monitoring. The YOLOv5 model has been used to detect humans in real- time video frames, and to obtain information on the detected bounding box for the bird’s eye view perspective technique. The pairwise distances of the identified bounding box centroid of people are calculated by utilizing euclidean distance. In addition, a threshold value has been set and applied as an approximation of social distance to pixels for determining social distance violations between people. The effectiveness of this proposed system is tested by experiments on different four video frames. The suggested system’s performance showed a high level of efficiency in monitoring social distancing accurately up to 100%.

Paper Citation

in Bibtex Style

@conference{visapp24,
author={Gona Rozhbayani and Amel Tuama and Fadwa Al-Azzo},
title={Social Distancing Monitoring by Human Detection Through Bird’s-Eye View Technique},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP},
year={2024},
pages={306-313},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012373900003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP
TI - Social Distancing Monitoring by Human Detection Through Bird’s-Eye View Technique
SN - 978-989-758-679-8
AU - Rozhbayani G.
AU - Tuama A.
AU - Al-Azzo F.
PY - 2024
SP - 306
EP - 313
DO - 10.5220/0012373900003660
PB - SciTePress

in Harvard Style

Rozhbayani G., Tuama A. and Al-Azzo F. (2024). Social Distancing Monitoring by Human Detection Through Bird’s-Eye View Technique. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 306-313. DOI: 10.5220/0012373900003660