UAV-Based Crowd Surveillance in Post COVID-19 Era

Abstract

Since outdoor events are gradually allowed within the current pandemic situation, a close monitoring of the crowd activity is needed to avoid undesired contact and disease transmission. In this context, unmanned aerial vehicles (UAVs) can be occasionally used to watch these activities, to ensure that health measures are applied, and to trigger alerts when an anomaly is detected. Consequently, we propose in this paper a complete UAV framework for intelligent monitoring of post COVID-19 outdoor activities. Specifically, we propose a three-step approach. In the first, captured images are analyzed using machine learning to detect and locate individuals. The second step consists of a novel coordinates mapping approach to evaluate distances among individuals and cluster them, while the third step provides an energy-efficient and reliable UAV trajectory to further inspect clusters for restrictions violation. Obtained results provide important insights towards the efficient design of the framework: 1) Efficient detection of individuals depends on the angle from which the images were captured, 2) coordinates mapping is very sensitive to estimate errors in individuals’ bounding boxes, and 3) UAV trajectory design algorithm 2-Opt is recommended for practical real-time deployments due to its low-complexity and near-optimal performance.

Keywords

• Object detection
• clustering
• unmanned aerial vehicle
• computer vision
• image coordinates mapping