Social Distancing Detection Using Computer Vision

Abstract

Abstract: SARS-CoV-19 is one of the deadliest pandemics the world has witnessed, taking around 6 crore lives till now across worldwide and about 6 lakhs in India. To limit its spread numerous countries have issued many safety measures. Due to the absence of the vaccine against the covid-19 the world has suffered a lot. Though scientists have developed several vaccines then also the pandemic is still out of control so therefore the only feasible option available to us is social distancing. We are implementing a Deep-learning based solution proposed for the above-stated problem. The distance between people can be estimated and the pair of people in the display will be indicated with red or green bounding boxes over it. The video frame from the camera was used as input, and the open-source object detection pre-trained model based on the YOLOv4 / YOLOv5 algorithm was employed for pedestrian detection. Later, the video frame was transformed into a top-down view for distance measurement from the 2D plane. The connection of CCTV cameras in public areas, public transportation, and hospitals is useful for gathering information. The proposed method was validated on a pre-recorded video of pedestrians walking on the street. Keywords: Deep learning, YOLOv4 / YOLOv5 algorithms, Social Distancing, Covid - 19