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학술대회 A Study on Feature Extraction Methods Used to Estimate a Driver’s Level of Drowsiness
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이경희, 김휘, 최현균, 장병태
International Conference on Advanced Communications Technology (ICACT) 2019, pp.710-713
18ZS1300, 주력 산업 고도화를 위한 지능형 상황인지 기반 기술 개발, 장병태
Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information that will improve road safety. A driver's condition can be estimated not only by basic characteristics such as gender, age, and driving experience, but also by a driver's facial expressions, bio-signals, and driving behaviours. Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy. Therefore, based on the relationship between facial features and a driver's drowsy state, variables that reflect facial features have been established. In this paper, we proposed a method for extracting detailed features of the eyes, the mouth, and positions of the head using OpenCV and Dlib library in order to estimate a driver's level of drowsiness.
KSP 제안 키워드
Autonomous vehicle, Basic characteristics, Driving experience, High accuracy, Machine Learning Methods, Recent developments, Road safety, Video processing, bio-signal, facial expression, facial features