눈 영상비를 이용한 운전자 상태 경고 시스템

This paper introduces the implementation of a driver’s condition warning system using eye aspect ratio to prevent a car accident. The proposed driver’s condition warning system using eye aspect ratio consists of a camera that is required to detect eyes, a raspberrypie that processes information on eyes from the camera, buzzer, and vibrator that are required to warn the driver. In order to detect and recognize driver’s eyes, the histogram of oriented gradients and face landmark estimation based on deep-learning are used. Initially the system calculates the eye aspect ratio of the driver from 6 coordinates around the eye and then gets each eye aspect ratio values when the eyes are opened and closed. These two different eye aspect ratio values are used to calculate the threshold value that is necessary to determine the eye state. Because the threshold value is adaptively determined according to the driver’s eye aspect ratio, the system can use the optimal threshold value to determine the driver’s condition. In addition, the system synthesizes an input image from the gray-scaled and LAB model images to operate in low lighting conditions.