Subjective analysis of social distance monitoring using YOLO v3 architecture and crowd tracking system

The lethal infection, World Health Organization (WHO) reported coronavirus (COVID-19) as a pandemic. Lack of proper vaccine, low levels of immunity against COVID-19 has led to vulnerability of the human beings. Due to lack of efficient vaccine treatment, the only options left to fight against this pandemic are lockdown and social distance. This work offers an autonomous monitoring system on social distancing using deep learning techniques. The proposed architecture tracks the humans on roads and calculates their distance between each other. This surveillance detects the furore violation of social distance utilizing CCTV cameras. The proposed framework uses YOLO v3 object-detection model built on COCO dataset and used to classify human class among 79 classes. The bounding box's dimensions and centroid coordinates are computed in the two-dimensional feature space from the pairwise vectorized L2 norm and a threshold is fixed for computing the distance maintained between each other. We illustrate the superior performance of our framework checked against other state of the art methods regarding inference speed, mean average precision and loss defined from the localization.