When COVID-19 spread all over the world it was important that people avoid social contact. This research paper proposes a YOLO-based deep learning method through which pedestrian are detected and hence social distancing can be monitored. Technique deployed in this research uses OpenCV along with deep learning for tracking the distance between individuals so that social contact can be monitored. Novelty of the work involves examining the viability of existing deep learning techniques over the UAV videos, hence, experiments have been performed on drone collected videos and further implementing object detection methods over them to calculate the distance between individuals. Performance analysis reveal that the proposed method has achieved IOU range of 0.5–0.9 with the Confidence Score of 0.2–0.9. The precision, recall, and F1-score of social distance detector have been obtained as 0.88, 0.9 and 0.8, respectively.