A Social Distance Monitoring System to ensure Social Distancing in Public Areas

TLDR: In this article, the authors proposed a system which is useful in monitoring public places like ATMs, malls and hospitals for any social distancing violations, where a simulated model uses deep learning algorithms with OpenCV library and a YOLO model trained on COCO dataset to identify people in the frame.

Abstract: Social distancing measures are important to reduce Covid spread. In order to break the chain of spread, social distancing is strictly followed as a norm. This paper demonstrates a system which is useful in monitoring public places like ATMs, malls and hospitals for any social distancing violations. With the help of this proposed system, it would be conveniently possible to monitor individuals whether they are maintaining the social distancing in the area under surveillance and also to alert the individuals as and when there is any violations from the predefined limits. The proposed deep learning technology based system can be installed for coverage within a certain limited distance. The algorithm could be implemented on the live images of CCTV cameras to perform the task. The simulated model uses deep learning algorithms with OpenCV library to estimate distance between the people in the frame, and a YOLO model trained on COCO dataset to identify people in the frame. The system has to be configured according to the location it is being installed at. By implementing the algorithm, the number of violations are reported based on the distance and set threshold. Number of violations reported are one and two for two real time images respectively. The red box highlighting the violations are displayed along with distance. Reporting efficiency and correctness were validated for more number of samples.