Abstract
Despite wide spread advances in the car manufacturing industries around the world, the death toll from car accidents is worrying. This concern is heightened when reports of a road accident website reports that the main reason in 25% of road accidents in particular and 60% to road accidents resulting in death or injury. Therefore, there has been a great deal of researches and functions for the automatic and machine detection of drowsiness that of them have reached the production stage. The present study is a method for positioning the pupil of the eye, determining the opening and closing of the pupil in real time and in unlimited environments, using the color feature in the first step, in Ybcbr color space and with the help of Gaussian function and Euclidean distance detection and then the area of the eye is positioned using the Viola jones algorithm. Finally, in order to locate the pupil and detect its openness, we have used two parallel Kalman filters. If the pupils are closed, they follow the Harris Eye Detection algorithm to identify the drowsiness of the driver and use this system to minimize the deaths caused by fatigue (tiredness) while driving. Detection is performed more precisely in the case of face rotation, different lighting conditions, and the eyes being closed or missing one of the eyes, the presence of glasses, beards, makeup, hijab, or obstruction of the eyes. The low computational complexity and maximum stability, real-time and unrestricted environment are other advantages of this method, all because the filters operate in parallel and do not even require high-resolution images.
Highlights
One of the most important contributors to traffic accidents, especially on intercity roads, is fatigue, drowsiness and lack of focus [1,2,3]
Researches show that the driver is usually tired after one hour of driving
Extracting the eye range This range starts from the top of the eyebrows to the bottom of the eyelid It includes two eyebrows and their distinctive features such as eyelids, corners of the eyes Using the horizontal face image obtained from Equation 5:
Summary
One of the most important contributors to traffic accidents, especially on intercity roads, is fatigue, drowsiness and lack of focus [1,2,3]. Many driver face monitoring systems only detect driver's fatigue and lack of focus based on features extracted from the eye. Steps of an Eye Tracking system: 1)Imaging 2) Face decryption 3) Eye decryption 4) Face tracing (detection)
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