Abstract There has been an alarming increase in the number of accidents that occur due to drowsiness while driving. In order reduce roadside accidents, detection driver fatigue or is crucial. Detecting during driving crucial for reducing as well improving safety both and passengers. Various methods can be used detect among drivers, but fuzzy logic-based stands out its ability avoid false alarms...

<span>Fatigue and drowsiness detection techniques based on the external features are under progress, methods of facial feature extraction require further development. This paper discusses innovative processes, efficient methods, recent advancements in field fatigue detection. In this proposed model, a wide application is planned artificial intelligence by defining fundamentals human-compu...

Currently, the traffic accidents study is become important because they produce several died and hurt around the world. In this paper, a new Driver Assistance System (DAS) for automatic driver’s drowsiness detection based on visual information is presented. This algorithm works on several stages. Once is to localize the face position using Viola and Jones (VJ) object detector. Another is to loc...

Accurate classification of eye state is a prerequisite for preventing automobile accidents due to driver drowsiness. Previous methods of classification, based on features extracted for a single eye, are vulnerable to eye localization errors and visual obstructions, and most use a fixed threshold for classification, irrespective of variations in the driver’s eye shape and texture. To address the...

In this paper, in order to implement a computer vision-based recognition system of driving fatigue. In addition to detecting human face in different light sources and the background conditions, and tracking eyes state combined with fuzzy logic to determine whether the driver of the physiological phenomenon of fatigue from face of detection. Driving fatigue recognition has been valued highly in ...

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...

Several factors often contribute to car accidents, most of them caused by human error, and the notable are drowsiness, fatigue, distracted driving, alcohol. Although self-driving cars best solution save lives avoid they expensive. The roads in many countries not prepared for movement this type car. Scare new technologies included modern cars, such as backup cameras sensors, contributed keeping ...