RESUMEN
In recent years, a lot of research works have been done on object detection using various machine learning models. However, not many works have been done on detecting and tracking humans in particular. This study works with the YOLOv4 object detector to detect humans to use the detections for maintaining social distance. For this study, the YOLOv4 model is trained on only one class named 'Person'. This is done to improve the speed of detecting humans in real time scenario with satisfying accuracy of 97% to 99%. These detections are then tracked to build a system for maintaining social distance and alerting the authority if a breach in the social distance is detected. This system can be applied at ticket counters, hospitals, offices, factories etc. It can also be used for maintaining social distance among the students and the teachers in the classroom for their safety. © 2022 IEEE.