Recently, in addition to research and development of autonomous vehicle technology, machine learning systems have been used to assess driver positions and emotions to upgrade road safety. The driver's position is assessed not only by basic characteristics such as gender, age, and driving experience, but also by the driver's facial expressions, biosignals, and driving behavior. Recent developments in video processing using machine learning have made it possible to analyze images obtained from cameras with high accuracy. Therefore, based on the relationship between facial features and the driver's sluggish position, variables that reflect facial features are established. In this paper, we propose a method of collecting detailed features of the eyes, mouth, and head using the OpenCV and DeLib library to assess the driver's level of drowsiness.
Driver Drowsiness Detection Using Haarcascade Algorithm
2020-08-05
International Journal of Research in Engineering, Science and Management; Vol. 3 No. 8 (2020); 59-60 ; 2581-5792
Article (Journal)
Electronic Resource
English
IEEE | 2020
|Springer Verlag | 2017
|Overview of Research on Driver Drowsiness Definition and Driver Drowsiness Detection
British Library Conference Proceedings | 1994
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