UAV-based Crowd Surveillance in Post COVID-19 Era
Nizar Masmoudi, Wael Jaafar, Safa Cherif, Jihene Ben Abderrazak, Halim, Yanikomeroglu

TL;DR
This paper presents a UAV-based framework for monitoring outdoor crowd activities post COVID-19, utilizing machine learning, coordinate mapping, and trajectory planning to ensure health restrictions are followed.
Contribution
It introduces a novel three-step UAV framework combining individual detection, distance evaluation, and energy-efficient trajectory planning for crowd surveillance.
Findings
Detection accuracy varies with camera angle.
Coordinate mapping is sensitive to bounding box errors.
2-Opt trajectory algorithm offers low complexity and near-optimal results.
Abstract
To cope with the current pandemic situation and reinstate pseudo-normal daily life, several measures have been deployed and maintained, such as mask wearing, social distancing, hands sanitizing, etc. Since outdoor cultural events, concerts, and picnics, are gradually allowed, a close monitoring of the crowd activity is needed to avoid undesired contact and disease transmission. In this context, intelligent unmanned aerial vehicles (UAVs) can be occasionally deployed to ensure the surveillance of these activities, that health restriction measures are applied, and to trigger alerts when the latter are not respected. Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities. Specifically, we propose a three steps approach. In the first step, captured images by a UAV are analyzed using machine learning to detect and locate…
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