E3S Web Conf.
Volume 351, 202210th International Conference on Innovation, Modern Applied Science & Environmental Studies (ICIES’2022)
|Number of page(s)||4|
|Published online||24 May 2022|
Social distance monitoring using YoloV4 on aerial drone images
1 Mohammed First University, ENSAO, AIRES laboratory, Oujda, Morocco
2 ATLAN space, Casablanca, Morocco
* Corresponding author: firstname.lastname@example.org
Monitoring social distancing in public spaces plays a crucial role in controlling and slowing the spread of the coronavirus during the COVID-19 pandemic. Using camera-equipped drone, the system presented in this paper detect unsafe social distance between people by applying deep learning algorithms namely the YoloV4 CNN algorithm to detect persons in images, in combination with trans-formation equations to calculate the real world position of each person, and finally calculate the distance between each pair in order to determine whether it is safe. We show also the results of training and testing a model using YoloV4 algorithm, and test the system for social distance calculation.
© The Authors, published by EDP Sciences, 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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