A study on warning system about drowsy status of driver

Nhan, Dinh Thanh; Bao, Truong Quoc; Dinh, Truong Quoc

doi:10.1109/icist.2017.7926759

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…To locate the potential eye positions and scales in the image is applied to the located eyes from facial points which are indicated for eyes. There are 6 points for each left and right eyes as shown in fig below The Eye Aspect Ratio is calculated as follows: leftEAR = eye_aspect_ratio(leftEye) rightEAR = eye_aspect_ratio(rightEye) ear = (leftEAR + rightEAR) / 2.0 The function eye_aspect_ratio calculated as: def eye_aspect_ratio(eye): A = dist.euclidean(eye [2], eye [6]) B = dist.euclidean(eye [3], eye [5]) C = dist.euclidean(eye [1], eye [4]) ear = (A + B) / (2.0 * C)…”

Section: F Eye Detection and Eye Openness Estimationmentioning

confidence: 99%

“… A Boost learning calculation to procure a mapping from a low-goal eye picture to a steady degree of eye transparency  A Computational calculation to acquire an exact and hearty eye receptiveness gauge dependent on versatile mix on multi-model eye recognitions on the two eyes. This paper hopes to develop an instance of a tired driver cautioning framework [3]. Our whole focus and obsession will be put on organizing the structure that will decisively control the open and close circumstance of the driver's eye ceaselessly.…”

Section: Introductionmentioning

confidence: 99%

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Driver State Alert Control using Head Shoulder Inclination and Facial Landmarks

Ebrahim¹,

Harshima²,

Abahai³

2020

IJEAT

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Driver sleepiness is one in all the most important causes of most of the accidents within the world. Detecting the driver’s eye weariness is the simplest way for detecting the somnolence of the driver. The prevailing systems in the literature cannot discover sleepiness in folks having lagophthalmos (condition that forestalls eyes from closing utterly) and monocular vision (person with one-eyed or sight loss in one eye). To solve this downside a Driver State Alert Control system is projected which makes the use of head-shoulder inclination, face detection, eye detection, emotion recognition, eye openness estimation and blink counts for detecting the sleepiness and collision liability associated with robust emotional factors. The projected framework endlessly analyses the head shoulder inclination and facial lineaments of the driver to alert the driver by activating the alarm once he/she is drowsy or showing emotion unstable to drive. The typical separation between upper eyelid and lower eyelid of adults suffering from lagophthalmos is about 1-5mm. The EAR (Eye Aspect Ratio) is calculated supporting this separately for each eye. Thus, the proposed technique can be used for folks with lagophthalmos and monocular vision. Also, the entities don’t solely rely on blink count to ascertain the sleepiness, collision risks related to robust emotional factors are considered too. The projected system which is enforced with one camera view on open CV and raspberry pi setting illustrates the systems good efficiency in particulars of authentic sleep identification results and thus reduces road mishaps. It is easy to place in any type of vehicle and price effective too.

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Section: F Eye Detection and Eye Openness Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Driver State Alert Control using Head Shoulder Inclination and Facial Landmarks

Ebrahim¹,

Harshima²,

Abahai³

2020

IJEAT

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“…One of the reliable features that can be used to represent the facial features are the shape features. [2,3,9] used shape feature namely HAAR, HOG and LBP respectively in extracting face features in measuring the fatigue and drowsy expression of driver. Moreover, [10][11][12] also used shape features based on Viola-Jones for extracting facial features for driver's fatigue and drowsy recognition.…”

Section: Introductionmentioning

confidence: 99%

“…[2,3] achieved 90% and 94.63% accuracy rate respectively in their application. Furthermore, [9,10,13] also adopted SVM and achieved accuracy rate more that 90%.…”

Section: Introductionmentioning

confidence: 99%

Driver’s Fatigue and Drowsy Expression Recognition Using Shape Features and Support Vector Machine

Mangshor

Yazid

Ibrahim

et al. 2021

Lecture Notes in Electrical Engineering

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The task of recognition and classification of driver's fatigue and drowsy face expression is a challenging task. There are several face parts involved in determining the fatigue and drowsiness levels of the driver. The face parts involved are eyes and mouths which portray different face expressions such as mouth open when yawning and shut or open eyes. Hence, it is very important to recognize the facial expression of the drivers based on these features. Therefore, this paper proposed the use of Shape Features for extracting the face features which are left eyes, right eyes and mouth. Next, Support Vector Machine (SVM) is deployed for the purposed of classification. Experiments have been conducted on the real-time images and promising result has achieved. The average classification accuracy achieved is 83.33%. Findings from this study is believed to expand the research towards building a fatigue and drowsy model for assisting driver in reducing traffic road accident.

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