Abstract

This project, introduces a user-friendly and efficient system to detect unusual activities in classrooms. By leveraging Python, YOLOv8, OpenCV, Face recognition, Haar Cascade classifiers, NumPy, and PyAudio, our approach covers five essential aspects: fight detection, fire detection, crowd detection, fall detection, noise monitoring. The crowd detection feature analyzes the density of people in the classroom, while fall detection identifies sudden posture changes for prompt response to potential accidents. The noise detection module categorizes unusual sounds, contributing to a safer learning environment. The fire and fight detection prevents happening of any emergency. This practical and adaptable solution enhances security and well-being in educational settings, offering a versatile tool for real-time monitoring and addressing diverse classroom scenarios. The modular codebase ensures scalability and the potential for future enhancements, emphasizing the project's applicability and longevity.