People Counting in Airports for COVID-19 Spreading Prevention using a Simple Computer Vision Algorithm

COVID-19 is an airborne disease that is particularly easy to be spread in indoor places. This is the case since people tend to get crowded and safety distances are not kept. Airports pose a major source of disease spreading since people from different places come together and reside in common places constituting them overcrowded at cases. Especially when people reside or queue up to certain places, (e.g. toilets, restaurants) the risk factor of getting the virus is high. Hence people occupancy, as well as queuing, need to get monitored to inform the passengers about the number of people that occupy a common place. To this end we propose a simple algorithm based on the Yolo8 for people detection, the DeepOCSort algorithm for tracking the people and our simple algorithm for counting the people. Video experiments showed that the algorithm can successfully fulfill the task of counting people.