An Efficient Drowsiness Detection Scheme using Video Analysis

Abstract

Road accidents caused due to drowsiness of the driver are quotidian. As per the world health organization (WHO) global report, India has the highest number of road accidents and about half or greater numbers are because of drowsy driving and this has become a major issue. The real-time drowsiness detection models detect when the driver is feeling drowsy by monitoring behavioral aspects or by using physiological sensors. Though the use of bio-sensors gives more accurate results, they are intrusive and distract the driver. In this research work, we have designed a behavioral-based drowsiness detection algorithm that monitors the movement of the face and the closeness of the eyes to detect and alert a drowsy driver. We also implemented the proposed algorithm using Matlab- 2021 and validated the efficiency of the proposed scheme where we took the live videos through the webcam and processed the frames in a frequent interval to assess whether the driver is drowsy or not. If drowsiness is detected, a system audio alert is generated to alert the driver. In case eyes or face are not detected in a frame, we by default classified it as drowsy and produced the alert message because a false negative is more dangerous than a false positive. All the evaluations of the proposed scheme are carried out using live videos and validated the results manually.