Student Monitoring System Using AI and Image Processing

The purpose of developing a Student Monitoring System that incorporates AI and Digital Image Processing is to automate the process of logging attendance, making it easier and more accurate to identify each student in the classroom or laboratory. The Student Monitoring System is built on a Raspberry Pi, which serves as the Central Processing Unit (CPU) of the system and stores current attendance data (logs) in a database created in conjunction with the RPI. An additional component of this system is an AI Vision Machine, which is used to identify each student's face when they attend class and is capable of providing audio announcements to students in class. Thus, the system will assist in eliminating the manual attendance logging method, resulting in a significant reduction of errors associated with this type of recording, saving time for both the student and the instructor. The Student Monitoring System allows the various components (AI algorithms, RPI, and database) used to identify students and the associated components of the system to work together with the latest data available for ongoing updates and monitoring. This solution was designed specifically for educational facilities to improve their attendance management processes and provide an enhanced student experience in a technology-driven campus environment.