DRONE POWERED CLASSROOM PRESENCE TRACKER

Abstract

In today's rapidly evolving educational landscape, the traditional methods of manual attendance tracking in classrooms are proving to be increasingly inefficient and prone to inaccuracies. This project introduces an innovative solution to modernize classroom attendance tracking through the integration of drone technology and advanced computer vision algorithms. By combining autonomous navigation, object detection, data processing, real-time reporting, and user interface functionalities, the system streamlines attendance management processes in educational environments. Utilizing drones equipped with advanced computer vision algorithms like YOLOv4, the system autonomously navigates classroom spaces to accurately detect and count individuals in real-time. The captured attendance data undergoes processing to filter irrelevant information, enabling educators and administrators to access up-to-date attendance information instantly through real-time reporting features. The user-friendly interface enhances accessibility and usability, making the system easily adaptable to educational settings of varying sizes and layouts. Additionally, the project's flexibility, precision, and proactive monitoring capabilities contribute to a more efficient, accurate, and proactive approach to attendance management, ultimately improving student engagement and success in the classroom. The proactive monitoring enabled by real-time reporting features allows educators to identify attendance trends promptly and intervene as needed to support student engagement and success. Keywords: Drone Technology, Computer Vision, Object Detection, YOLOv4, Real Time Reporting, Attendance Tracking