Monitoring of epidemiological restrictions during the pandemic for the sustainability of public transport system

In this article, a system which combines computer vision with electrical devices for enrichment is proposed in order to prevent risks that can occur in the future and contribute to reducing the spread of COVID-19 and other contagious infections that can happen in the future as well. According to the researches related to the current pandemic situation (COVID-19), the policies that should be taken are clear such as social distancing and wearing masks, but due to many reasons depending on human behaviors, people lose their attention to keep the rules. This system will help to prevent these risks. In this work, occupations that contact many people daily, but not under the control, sterilized area, and not affordable to work from home were considered as well as the target objects in our system. Contribution in this research work is to develop an expanded monitoring system which helps to prevent and reduce risk of the pandemic continuing currently and can occur in future by combining computer vision with electrical sensors to increase its efficiency. Machine learning based image recognition techniques were used to detect and monitor specified actions. In order to increase its functionality and efficiency, thermal sensors, camera, and microphones were deployed as well. Therefore, IoT was used for collecting the data result from monitoring for future applications.