A project report submitted in partial fulfillment of the requirements for B.Tech. Project.
COVID-19 is a respiratory disease that causes severe illness. It was discovered in December 2019 in Wuhan, China. It has resulted in a continuing pandemic with numerous cases of infection and deaths. Coronavirus is transmitted mostly through human-to-human contact. This study proposes a image-based artificial intelligence system for social distancing classification. The YOLOv5 approach is used to construct a revolutionary deep learning object detection technique for recognising and tracking individuals in indoor and outdoor environments. An algorithm is also used to automatically evaluate whether or not social distancing rules are being met, as well as to measure and evaluate the distance between people. As a result, the aim of the project is to discover if and how individuals adhere to social distancing norms in order to stop the COVID-19 virus from spreading. The suggested method is used to construct a comprehensive AI system for people tracking, social distance classification using camera photographs. Datasets obtained from distinct CCTV cameras are used in the training phase.