Indoor Crowd Interaction Surveillance Using Image Processing in Post-COVID-19 Situation

Abstract

Working title: Indoor crowd interaction surveillance using image processing in post-COVID19 situation Human interaction is limited in today’s society because of Covid 19 health restrictions, which are in place to prevent the virus from spreading. According to the rules, individuals must be at least one meter apart, and the number of individuals in an indoor environment is limited to a certain number. However, most people do not follow the instructions, putting the disease’s spread at risk. The severity is substantially higher if the environment is indoor. If a single infected person is detected in the area, health officials should trace the close contacts of the person. To answer this problem, the research project was conducted by providing a solution for contact trace. The research is conducted by implementing a convolutional neural network to obtain the risk footage from the CCTV footage and determine the health guideline violations. With the violated information digital contact tracing was done through the face search framework.