A Novel Approach for Drowsy Driver Detection Using Eyes Recognition System Based on Wavelet Network

Abstract

Drowsiness is defined as the "inclination to sleep" and is also commonly referred to as "sleepiness". It is a natural occurrence in the human body that can affect individuals in different ways. Driver impairment due to drowsiness is known to be a major contributing factor in many motor vehicle crashes. Hence, the need of a reliable driver drowsiness detection system which could alert drivers before a mishap happens. In this paper, a drowsy driver detection system has been developed, using video processing analyzing eyes blinking concepts for measuring eyes closure duration to verify the driver vigilance state. In such a case, when this duration exceeds a specific time, a warning signal is issued to alert the driver. To achieve this application, three main steps are required: face and eyes detection, classification and eyes tracking and measurement of eyes closure duration. To accomplish this work, eyes are tracked and recognized employing wavelet network classifier (WNC) based on fast wavelet transform (FWT) for its robustness and for its pertinent results in the classification's domain. Experiments show that our used methods are effective for driver drowsiness detection.

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

“…After locating the driver's face, the next step is to detect and track the location of the eyes, mouth, nose and other facial features, various methods used for this tasks such as Viola-Jones that is used in [37,39,47,77,94,95,96], which is one of the most successful tools for object recognition based on the Haar cascade feature approach and is frequently used for facial feature extraction (location of eyes, nose and mouth on face) and face detection. This algorithm uses 4 main factors namely the Haar feature, Integral Image, Cascade Classifier, and Adaboost machine-learning.…”

The Role of Sleep and Impact on Brain and Learning

“…In addition, hypo vigilance may cause micro-sleeps that may affect attention dangerously, during various activities. Special monitoring and devices may contribute to the decrease of such incidents [11], [12].…”

Driving posture recognition by convolutional neural networks

“…In an lth full connection layer, an error d j l+1 , is back propagated from an upper (l + 1)th layer, then the error d i l and the weight gradient Dw ij l in this layer is given by (13) and (14), respectively (14) where w ij is the m × n weight matrix, n and x i l are the total number of the output neuron and input neuron when feed forwarding, respectively. Δf acti (•) l is the gradients of the non-linearity activation function, the error d i l will be back propagated to the lower (l − 1)th layer.…”

“…An alternative way to analyse driver behaviour is using a camera. There are three categories of vision-based approaches to automatically monitor the unsafe driver behaviour: (i) gaze and head pose analysis for the prediction of driver behaviour and intention [5,[8][9][10][11], (ii) extraction of fatigue cues from driver facial image [6,[12][13][14], and (iii) characterisation (in the context of safe versus unsafe driving behaviour) of driver body postures, including the positioning of arms, hands, and feet [7,[15][16][17]. Despite the encouraging performances under appropriate conditions, the proposed approaches share a common disadvantage of being ad hoc.…”