COVID-19 is currently threatening human life, health, and productivity. Population vulnerability grows as a result of a lack of effective remedial agents and a scarcity of vaccines against the virus. As a result, social distancing is considered an adequate precaution (norm) against the pandemic virus's spread. The danger of viral propagation can be decreased by avoiding physical contact between people. This paper gives overview about how to automate the task of monitoring social distancing using input video and images, which is motivated by this idea. To distinguish humans from the background, this methodology employs the YOLO v4 model for detecting objects. With respect to mean average precision (mAP), frames per second (FPS), and loss values given by object classification and localization in input frame, the YOLO v4 model's results are compared to those of other popular models, such as FRCNN and SSD. The pairwise vectorized L2 norm is computed using the 3D feature space obtained by using the bounding box's centroid coordinates and dimensions. Random forest regression is used to predict violations using data collected from the Yolo v4 model. According to the results of the experimental analysis, YOLO v4 and random forest regression produced the best results with balanced mAP and FPS score to monitor and predict future possibility of infection using generated data.

Keywords : m YOLO-v4, Object Detection, OpenCV, Random Forest regression