Abstract: In This Period Maintaining social distancing norms between humans has become an indispensable precaution to slow down the transmission of SARS COVID-19. I present a novel method to automatically detect pairs of humans in a crowded scenario who are not adhering to the social distance constraint, i.e., about 6 feet of space between them. My approach Will makes no assumption about the crowd density or pedestrian walking directions. I use a mobile robot with commodity sensors, namely a camera and a 2-D lidar to perform collision-free navigation in a Crowd and estimate the distance between all detected individuals in the camera’s field of view. In addition, With It I also equip the robot with a thermal camera that wirelessly transmits thermal images to a security/healthcare personnel who monitors if any individual exhibits a higher-than-normal temperature. In indoor scenarios, this mobile robot can also be combined with static mounted cameras to further improve the performance in terms of number of social distancing Culprits detected, accurately pursuing walking pedestrians etc. I highlight the performance benefits of our approach in different static and dynamic indoor scenarios.

Keywords: SARS COVID-19 pandemic, Social Distancing, Collision Avoidance. Mobile Robot.

| DOI: 10.17148/IJIREEICE.2021.9613