Social Distancing Detection with Deep Learning Model
P Sanjeevi
Abstract
The social distancing detection using deep learning is to evaluate the distance between people to mitigate the impact of this coronavirus pandemic. The detection tool was developed to alert people to maintain a safe distance with each other. The open-source object detection pre-trained model based on the YOLOv3 algorithm was employed for pedestrian detection. The distance between people can be estimated and any noncompliant pair of people in the display will be indicated with a red frame. The number of people in an image and video with bounding boxes can be detected via the YOLO method which was employed to detect the video stream taken by the camera. We will be using YOLOv3, trained on COCO dataset for object detection. In general, single-stage detectors like YOLO tend to be less accurate than two-stage detectors and are significantly faster. YOLO treats object detection as a regression problem, taking a given input image and simultaneously learning bounding box coordinates and corresponding class label probabilities.