Real-Time Drowsiness Detection System for Student Tracking using Machine Learning

Abstract

Many studies on fatigue detection have been carried out that were focused on experimention over different technologies. Machine vision based driver fatigue detection systems are used to prevent accidents and improve safety on roads. We propose the design of an alerting system for the students that will use real time video of a person to capture the drowsiness level and will signal alert to the student when the student is in that state of fatigue. A device, if enabled with the system, will start the webcam and track the person. An alert will be generated based on the set frame rate when a continuous set of frames are detected as drowsy. The conventional methods cannot capture complex expressions, however the vailability of deep learning models has enabled a substantial research on detection of states of a person in real time. Our system operates in natural lighting conditions and can predict accurately even when the face is covered with glasses, head caps, etc. The system is implemented using YOLOv5 models (You Look Only Once) is an extremely fast and accurate detection model.