ABSTRACT
Currently, the prevention of the spread of the Covid-19 epidemic is still a matter of concern with many new variants that are more infectious and making it more difficult to prevent it. In addition, several respiratory viral diseases such as influenza A, monkeypox, etc. help promote the management and prevention of epidemics. The paper presents the system using the YOLOV4 object recognition model to identify human objects from videos extracted. To increase accuracy with the desired context, we build a dataset of people and perform training on them. We use the Euclidean algorithm to calculate the distance between bounding box pairs. We then use a physical distance that approximates the pixel and set a threshold. It is possible to determine who has violated the minimum social distance threshold. In addition, we apply a tracking algorithm to be able to detect and trace those who have been in close contact with the cases to be monitored. The system has been performed on video and the accuracy of the model is up to 95.6%. © 2022 IEEE.