A Machine Learning-Driven Drone Surveillance For Student Monitoring in Educational Institutions

This project leverages advanced machine learning techniques and drone technology to enhance surveillance within academic environments. A drone equipped with a camera captures images of individuals and sends them to a machine learning model that has been pre-trained on a dataset containing personal details such as images, roll numbers, and names. The system compares the captured images with the stored data to identify individuals. If a match is detected and the person is found outside their designated classroom during class hours, the system automatically notifies the respective class teachers. This innovative approach demonstrates the effective use of machine learning and drones as tools for real-time monitoring. This project leverages machine learning techniques to enhance surveillance and ensure compliance within academic institutions. Index Terms - Drone Technology, Image Recognition, Real-Time Monitoring, Dataset Comparison, Academic Compliance, Automated Notification, Classroom Attendance Tracking