Abstract:
The COVID – 19 pandemic has become the world current emergency public health issue. Rise of COVID – 19 cases have urged the importance and necessity of health protocol implementation. Despite the implementation of health protocol, some people tend to disregard it. Crowd gathering still happens and some people are not even wearing masks which gradually will increase the number of COVID – 19 cases. This indicates that the current way of crowd monitoring is not quite effective to prevent the spreading of COVID – 19. Therefore, an automated system to perform crowd monitoring and detection is needed to maximize and ensure the implementation of health protocol is conducted as expected.
This thesis will propose a solution to solve the identified problem with the title of “COVID – 19 Crowd Monitoring and Detection Using Raspberry Pi”. The system will be integrated with a Raspberry Pi as an IoT (Internet of Things) solution. The system will be using deep learning techniques to process the image captured from the camera to analyze and perform detection in several aspects. The main features of the system cover crowd detection and monitoring the number of people in a crowd, detecting whether people in the crowd are wearing masks or not, and detecting their body temperature. An alert feature will be integrated to provide warning if there is a crowd detected, people not wearing masks, and having high temperature. The related parties or authorities using this application can be notified through the alert feature. In addition, the results of processed data from analyzing the crowd situation will be displayed on a web application to enable users to monitor it.
