Abstract
The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.
Highlights
Driver drowsiness and fatigue are one of the most common reasons for accidents
This paper aims to lessen the number of accidents due to driver drowsiness and fatigue
Driver drowsiness detection is a technology in vehicles that is useful in preventing accidents and saving the lives of drivers when they are getting drowsy
Summary
Driver drowsiness and fatigue are one of the most common reasons for accidents. The number of fatalities due to such accidents is increasing worldwide each year. This paper aims to lessen the number of accidents due to driver drowsiness and fatigue. This will in turn increase transportation safety. Driver drowsiness detection is a technology in vehicles that is useful in preventing accidents and saving the lives of drivers when they are getting drowsy. This project uses computer vision for the detection of drivers’ drowsiness. With the constant improvement and novelty in technology, there is an advancement in transportation modes Our dependencies on it have started increasing at a high rate. The existing technologies to detect driver drowsiness are either very costly systems that apply to the high-end car models or systems that are affordable but are not robust
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