Detecting Driver Drowsiness Using Eye State
نویسندگان
چکیده
منابع مشابه
Driver Drowsiness Detection by Identification of Yawning and Eye Closure
Today most accidents are caused by drivers’ fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’s mouth and eyes for detecting drowsiness. At first, using the depth informa...
متن کاملMachine Learning Systems for Detecting Driver Drowsiness
The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to data-mi...
متن کاملDriver drowsiness monitoring using eye movement features derived from electrooculography
The increase in vehicle accidents due to the driver drowsiness over the last years highlights the need for developing reliable drowsiness assistant systems by a reference drowsiness measure. Therefore, the thesis at hand is aimed at classifying the driver vigilance state based on eye movements using electrooculography (eog). In order to give an insight into the states of driving, which lead to ...
متن کاملDetecting Driver Drowsiness Based on Sensors: A Review
In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehic...
متن کاملDriver Drowsiness Detection System Using Image Processing
Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist ...
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ژورنال
عنوان ژورنال: DEStech Transactions on Computer Science and Engineering
سال: 2018
ISSN: 2475-8841
DOI: 10.12783/dtcse/csae2017/17533