2 research outputs found

    ODU Undergraduate Students Addressing the Societal Problems of Parking Control, Classroom Seating, and Flood Monitoring in Hampton Roads

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    During the summer of 2021, ODU undergraduate computer science students undertook image processing research projects. These projects focused on utilizing the Raspberry Pi computer and camera module to address three real-world problems concerning parking control, classroom seating, and flood monitoring. The parking lot occupancy project aimed to develop a system that monitors the occupancy of parking spaces in a lot and communicates the status of the lot of drivers and the lot attendants. The COVID-19 classroom occupancy project sought to enforce social distancing protocols in a classroom environment by detecting seating violations and notifying the instructor and the impacted students of the violation. Designed for the Hampton Roads community, the flood detection project concerned the development of a vision system, controlled by the Raspberry Pi, that detects the flood levels of a particular location and determines if the flooding is low, moderate, or severe. This paper details the development of these projects and proposes future considerations and recommendations for further undergraduate study and improved real-world functionality

    COVID-19 Crowd Detection

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    Object detection was introduced by researchers for face detection. Researchers explain how the detected face is divided into minor frames to be recognized by the algorithm. Due to COVID-19 and government regulations, many people face problems going to shopping centers and shop safely. It has been very hard for both the government and the people to manage social distancing. In our study, we developed a system using Raspberry Pi-4 that will detect the distance between people along with counting the number of distance and mask violations. An error message will appear on the screen in red, showing the total number of distance and mask violations, which could later be used by the customer as statistical evidence for better safety precautions
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