Driver Drowsiness Detection using Machine Learning with Visual Behaviour
نویسندگان
چکیده
منابع مشابه
Machine 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...
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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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now a day’s Road accidents are common in developed as well as developing countries. These accidents happen due to different different reasons like sleeping disorders, working in night shift or more than eight hours as over time, side effects of medicine, alcohol, speeding, freakishness of teenager’s etc. One of the most important reasons is drowsiness. Drowsiness means sleepiness, which affects...
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In order to give a warning to the drowsy driver, this paper proposes a new Fatigue Driving Detection Algorithm. AdaBoost algorithm is applied to fast detect and track human faces, and the algorithm is implemented in FPGA; differential template-based multi-algorithms are used to localize human eyes and recognize eye states; PERCLOS algorithm is adopted to analyze and determine whether a person i...
متن کاملDriver 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...
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ژورنال
عنوان ژورنال: International Journal for Research in Applied Science and Engineering Technology
سال: 2021
ISSN: 2321-9653
DOI: 10.22214/ijraset.2021.35348