The supervision of people in special circumstances, having as their purpose detecting risk situations, is an area which has many applications. In this context we are interested, in this paper, the problems of driver drowsiness detection based on video analysis. We describe a new approach to detect reduced alertness automatically, to prevent the driver. Unlike many other works, our noninvasively...

In the present trend suggestsdriving and navigation support systems are getting importance because it is crucial in supporting drivers in several conditions in automobile industry. It is important for driving support systems to detect the status/activity of driver’s consciousness. Detecting onset of driver fatigue could prevent the accidents caused by drowsy driving. It is proposed to detect / ...

The risks involved in nighttime driving include drowsy drivers and dangerous vehicles. Prominent among the more dangerous vehicles around at night are the larger vehicles which are usually moving faster at night on a highway. In addition, the risk level of driving around larger vehicles rises significantly when the driver's attention becomes distracted, even for a short period of time. For the ...

Driver drowsiness is a major cause of fatal accidents, injury, and property damage, and has become an area of substantial research attention in recent years. The present study proposes a method to detect drowsiness in drivers which integrates features of electrocardiography (ECG) and electroencephalography (EEG) to improve detection performance. The study measures differences between the alert ...

The driver sleepiness is common and one of the main causes road accidents. So, there a need for automatically detecting this human behaviour. In any case, if drivers feel drowsy, they still keep on driving vehicle, accidents occur. This study can be implemented using CNN training model initiating an alarm drowsiness condition detected. Many authors suggested process Drowsiness (Problem Statemen...

The proposed system aims to enhance transportation safety by reducing the number of accidents caused driver fatigue and drowsiness, which have become increasingly common. analyzes facial bodily gestures such as eye tiredness yawning identify signs drowsiness fatigue. Eye aspect ratio (EAR) is used detect measuring distances between horizontal vertical landmarks. Yawn detection performed calcula...

As technology and artificial intelligence conquer a place under the spotlight in automotive world, driver drowsiness monitoring systems have sparked much interest as way to increase safety avoid sleepiness-related accidents. Such technologies, however, stumble upon observation that each presents distinct set of behavioral physiological manifestations drowsiness, thus rendering its objective ass...