2024/07/02 by Ali Akin, Akin, Ali, Habil Kalkan +1
Engineering · Psychology · #Computer Vision and Pattern Recognition (cs.CV) #Ergonomics and Musculoskeletal Disorders #FOS: Computer and information sciences #Sleep and Work-Related Fatigue #Traffic and Road Safety
paper · pdf · doi:10.48550/arxiv.2407.02222
openalex publication_date 2024/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traffic accidents, causing millions of deaths and billions of dollars in economic losses each year globally, have become a significant issue. One of the main causes of these accidents is drivers being sleepy or fatigued. Recently, various studies have focused on detecting drivers' sleep/wake states using camera-based solutions that do not require physical contact with the driver, thereby enhancing ease of use. In this study, besides the eye blink frequency, a driver adaptive eye blink behavior feature set have been evaluated to detect the fatigue status. It is observed from the results that behavior of eye blink carries useful information on fatigue detection. The developed image-based system provides a solution that can work adaptively to the physical characteristics of the drivers and their positions in the vehicle