A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models

The World Health Organization (WHO) has suggested a successful social distancing strategy for reducing the COVID-19 virus spread in public places. All governments and national health bodies have mandated a 2-m physical distance between malls, schools, and congested areas. The existing algorithms proposed and developed for object detection are Simple Online and Real-time Tracking (SORT) and Convolutional Neural Networks (CNN). The YOLOv3 algorithm is used because YOLOv3 is an efficient and powerful real-time object detection algorithm in comparison with several other object detection algorithms. Video surveillance cameras are being used to implement this system. A model will be trained against the most comprehensive datasets, such as the COCO datasets, for this purpose. As a result, high-risk zones, or areas where virus spread is most likely, are identified. This may support authorities in enhancing the setup of a public space according to the precautionary measures to reduce hazardous zones. The developed framework is a comprehensive and precise solution for object detection that can be used in a variety of fields such as autonomous vehicles and human action recognition.