IoT and AI Based Recognition and Classification of Covid 19 Persons in Public Place

Abstract

One of the computer vision application is human face detection. There have been several studies in the field of image processing on the face. Previously, several researchers studied facial recognition. In this paper, we identified the covid-19 patient roaming in public places during quarantine time, which has been identified by using IOT and AI techniques with core concept of Human face detection. This is the new idea in this covid -19 conditions for Human face detection. In district wise, day by day regular covid -19 positive cases are stored in the cloud by using an IoT mechanism. The storable data’s such as name, mobile number, address with photos (with different poses). These personal details are properly stored and retrieved from the cloud database. The store and retrieve process are handled by using IoT with Raspberry Pi. In CCTV with face detection system is used to observe the actual situation and detect any human presence on the video. We set up the cameras in important places that are connected to the cloud server to forward the covid 19 affected and non-covid person’s faces. In this recognition, processes are handled by using AI techniques and the classification of covid positive and normal case by using convolution neural network (CNN). The roaming persons are captured in the camera continuously, AI technique will match and classify the face with already stored database (testing center data). In this classification process, If AI recognizes the covid positive patient, raspberry pi will follow the classified personal data that will directly send a message to the government health care unit, they will take legal action against the person. This experiment, we have conducted by using OpenCV with python platform. This proposed model will minimize the covid 19 spread in public, and also decrease the mortality rate due to covid disease.