COVID-19 Social Distancing Surveillance System

Abstract

The pandemic from COVID-19 impinged our day-to-day lives and wreaked havoc upon many sectors in our society. This worldwide pandemic, which had its onset in January 2020, has forced us to reconsider our perception of what “normal” should be. While there’s no official cure yet, various vaccines have been rolled out and are expected to take effect soon. However, the efficacy of vaccines has been a debatable issue. Thus, the most effective way to battle this situation would be to strictly follow the precautionary measures advised by the governing authorities. Wearing mask and following the social distancing norms are considered as one of the most effective ways to control the spread of infection [1]. However, this new normal becomes difficult to implement as many people tend not to follow social distancing. While it is difficult to check whether people are following social distancing, we propose a solution which would come in handy in such circumstances and would hasten the process of contact tracing in comparison to manual inspection. In this study, we strive to present a video surveillance model, which would allow the detection of social distancing between people based on object detection and tracking algorithms. The specific algorithm used in our study for object detection is the YOLO algorithm and monitoring the distance between any two persons is done using a technique called Perspective Transformation. The proposed method shows promising results which could be implemented as a surveillance system for monitoring social distancing.