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The rampant coronavirus disease 2019 (COVID-19) has brought global crisiswith its deadly spread to more than 180 countries, and about 3,519,901confirmed cases along with 247,630 deaths globally as on May 4, 2020. Theabsence of any active therapeutic agents and the lack of immunity againstCOVID-19 increases the vulnerability of the population. Since there are novaccines available, social distancing is the only feasible approach to fightagainst this pandemic. Motivated by this notion, this article proposes a deeplearning based framework for automating the task of monitoring socialdistancing using surveillance video. The proposed framework utilizes the YOLOv3 object detection model to segregate humans from the background and Deepsortapproach to track the identified people with the help of bounding boxes andassigned IDs. The results of the YOLO v3 model are further compared with otherpopular state-of-the-art models, e.g. faster region-based CNN (convolutionneural network) and single shot detector (SSD) in terms of mean averageprecision (mAP), frames per second (FPS) and loss values defined by objectclassification and localization. Later, the pairwise vectorized L2 norm iscomputed based on the three-dimensional feature space obtained by using thecentroid coordinates and dimensions of the bounding box. The violation indexterm is proposed to quantize the non adoption of social distancing protocol.From the experimental analysis, it is observed that the YOLO v3 with Deepsorttracking scheme displayed best results with balanced mAP and FPS score tomonitor the social distancing in real-time.

Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques