An Efficient Drowsiness Detection Scheme using Video Analysis

• Published in: International Journal of Computing and Digital System (Jāmiʻat al-Baḥrayn. Markaz al-Nashr al-ʻIlmī) Vol. 11; no. 1; pp. 573 - 581
• Main Authors: Murthy, K. Sree Rama, Siddineni, Bhavana, Kompella, Vijay Kashyap, Aashritha, Kondaveeti, Sri Sai, Boddupalli Hemanth, Manikandan, V. M.
• Format: Journal Article
• Language: English
• Published: University of Bahrain, Deanship of Graduate Studies and Scientific Research 2022
• Subjects: Accidents; Algorithms; Physiological aspects; Sensors; India
• ISSN: 2210-142X; 2210-142X
• DOI: 10.12785/ijcds/110146

Road accidents caused due to drowsiness of the driver are quotidian. As per the World Health Organization global report, India has the highest number of road accidents, and about half or greater number are because of drowsy driving, and this has become a major issue. Real-time drowsiness detection models detect when the driver is feeling drowsy by monitoring behavioural aspects or by using physiological sensors. Though the use of bio-sensors gives more accurate results, they are intrusive and distract the driver. We have developed and implemented a behavioural-based drowsiness detection algorithm that monitors the movement of the face and closeness of eyes to detect and alert a drowsy driver. We successfully implemented our algorithm in Matlab-2020 software, where we took a live video from a webcam and processed each frame to classify it as either drowsy or not. We also tested on a dataset featuring live driving subjects and achieved 90% accuracy with 84% precision. 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, and thus attained a high recall of 98%. Keywords: Drowsiness detection, Face movement detection, Eye closeness detection, Viola-Jones algorithm, SVM classifier