Description/Abstract/Artist Statement

The classroom occupancy detection system aims to limit the spread of COVID-19 and support mitigation efforts advised by national and international health organizations by enforcing social distancing in classroom environments. Utilizing the RaspberryPi computer and its compatible camera module, the system accomplishes this by capturing an overhead image of a classroom and assessing the image for violations. Here, violations are defined as the presence of adjacent occupied seats. As such, for an acceptable state to be detected, there must be at least one vacant seat between all students seated in the classroom. The system communicates the classroom’s state with two light-emitting diode circuits, illuminating a green LED to denote an acceptable state and a red LED to denote one or more violations. System performance was evaluated under three test case scenarios with a simulated classroom environment. The test case results revealed that the system can accurately detect acceptable conditions, as well as the presence of one or more seating violations. However, the inability to account for human behaviors and complex seating layouts limits the system’s real-world functionality. Despite its current limitations, this project suggests that image processing techniques may be a feasible solution to support social distancing in the classroom.

Presenting Author Name/s

Stephanie Trusty

Faculty Advisor/Mentor

Ayman Elmesalami, Soad Ibrahim

College Affiliation

College of Sciences

Presentation Type

Oral Presentation

Disciplines

Other Computer Sciences

Session Title

Colleges of Sciences UG Research #1

Location

Zoom

Start Date

3-19-2022 1:00 PM

End Date

3-19-2022 2:00 PM

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Mar 19th, 1:00 PM Mar 19th, 2:00 PM

COVID-19 Classroom Occupancy Detection System

Zoom

The classroom occupancy detection system aims to limit the spread of COVID-19 and support mitigation efforts advised by national and international health organizations by enforcing social distancing in classroom environments. Utilizing the RaspberryPi computer and its compatible camera module, the system accomplishes this by capturing an overhead image of a classroom and assessing the image for violations. Here, violations are defined as the presence of adjacent occupied seats. As such, for an acceptable state to be detected, there must be at least one vacant seat between all students seated in the classroom. The system communicates the classroom’s state with two light-emitting diode circuits, illuminating a green LED to denote an acceptable state and a red LED to denote one or more violations. System performance was evaluated under three test case scenarios with a simulated classroom environment. The test case results revealed that the system can accurately detect acceptable conditions, as well as the presence of one or more seating violations. However, the inability to account for human behaviors and complex seating layouts limits the system’s real-world functionality. Despite its current limitations, this project suggests that image processing techniques may be a feasible solution to support social distancing in the classroom.