Safe Zone Camera

Abstract

Human Health is turning into a major issue even in this cutting-edge period of innovation. The COVID-19 that was declared a pandemic by the World Health Organization (WHO) on March 13, 2020, after the widespread of the disease in Europe, and the drastic number of deaths in Italy is exceptionally transmissible. The current scenario has shown quite a noticeable impact on people's mental well-being besides their physical well-being. It has changed their perception about life, and their priorities regarding daily life routine have also been affected. Such a global situation can only be controlled with people's consent to behave in a particular manner instructed by health care providers such as frequent hand washing, using the facemask, avoiding gatherings, and maintaining permissible distance. But we seem to have no such mechanism to avoid such situations. So, out project reiterates on creating a device which can help in detecting the amount of people who are following SOPs regarding wearing of face mask through camera feed. This can assist in minimizing the damage caused by COVID-19. The device has two modules, one is the device itself consisting of Nvidia Jetson Nano which will be connected to Camera and a Display Screen, the second one is the training model mainly Yolo v5. The Camera live feed is sent to the Nvidia Jetson which will process the video, frames by frames and by using YoloV5 trained models will identify the person wearing masks or not. The authorities can view the detailed aspect on the Display Screen. The principle preferred standpoint of this framework is that the client does not require an expensive device to differentiate between Masked and Nonmasked persons dissimilar to different devices that have been developed before. Furthermore, utilization of refined segments guarantees exactness and makes it robust.