Real Time Eye Tracking and Detection- A Driving Assistance System

Said, Sherif; Kork, Samer Al; Beyrouthy, T.; Hassan, M. F.; Abdellatif, OE; Abdraboo, M Fayek

doi:10.25046/aj030653

Cited by 16 publications

(4 citation statements)

References 16 publications

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“…tinuous reflection of lights on the eye cannot be assured. The detection of driver's fatigue [119], monitoring of drowsiness [120], detection of drowsiness in real time [121], drivingassistance systems [122], and accident prevention [123] all benefit from eye tracking research. Many researchers have expressed interest in creating intelligent systems for activating alert systems when the driver's eyes are not visible for several seconds.…”

Section: Support Vectormentioning

confidence: 99%

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

et al. 2022

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Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research.

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Section: Support Vectormentioning

confidence: 99%

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

et al. 2022

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“…Mandal et al [24] have been proposed a vision-based fatigue identification method for bus driver monitoring. In this work, AHOG and SVM are used respectively for headshoulder detection and driver detection.…”

Section: Literatue Reviewmentioning

confidence: 99%

Real-time Driver Drowsiness Detection using Deep Learning

Dipu¹,

Hossain²,

Arafat³

et al. 2021

IJACSA

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Every year thousands of lives pass away worldwide due to vehicle accidents, and the main reason behind this is the drowsiness in drivers. A drowsiness detection system will help to reduce this accident and save many lives around the world. To defend this problem, we propose a methodology based on Convolutional Neural Networks (CNN) that illustrates drowsiness detection as a task to detect an object. It will detect and localize whether the eyes are open or close based on the realtime video stream of drivers. The MobileNet CNN Architecture with Single Shot Multibox Detector is the technology used for this object detection task. A separate algorithm is used based on the output given by the SSD_MobileNet_v1 architecture. A dataset that consists of around 4500 images was labeled with the object's face yawn, no-yawn, open eye, and closed eye to train the SSD_MobileNet_v1 Network. Around 600 randomly selected images are used to test the trained model using the PASCAL VOC metric. The proposed approach is to ensure better accuracy and computational efficiency. It is also affordable as it can process incoming video streams in real-time and does not need any expensive hardware support. There only needs a standalone camera to be implemented using cheap devices in cars using Raspberry Pi 3 or other IP cameras.

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“…An Eye-tracking based driver drowsiness system was proposed by Said et al [16]. In this work the system finds the driver's drowsiness and rings the alarm to alert to the driver.…”

Section: Related Workmentioning

confidence: 99%

Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

Chirra¹,

Reddy²,

Kolli³

2019

RIA

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Driver drowsiness is one of the reasons for large number of road accidents these days. With the advancement in Computer Vision technologies, smart/intelligent cameras are developed to identify drowsiness in drivers, thereby alerting drivers which in turn reduce accidents when they are in fatigue. In this work, a new framework is proposed using deep learning to detect driver drowsiness based on Eye state while driving the vehicle. To detect the face and extract the eye region from the face images, Viola-Jones face detection algorithm is used in this work. Stacked deep convolution neural network is developed to extract features from dynamically identified key frames from camera sequences and used for learning phase. A SoftMax layer in CNN classifier is used to classify the driver as sleep or non-sleep. This system alerts driver with an alarm when the driver is in sleepy mood. The proposed work is evaluated on a collected dataset and shows better accuracy with 96.42% when compared with traditional CNN. The limitation of traditional CNN such as pose accuracy in regression is overcome with the proposed Staked Deep CNN.

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Real Time Eye Tracking and Detection- A Driving Assistance System

Cited by 16 publications

References 16 publications

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

Eye-Tracking Assistive Technologies for Individuals With Amyotrophic Lateral Sclerosis

Real-time Driver Drowsiness Detection using Deep Learning

Deep CNN: A Machine Learning Approach for Driver Drowsiness Detection Based on Eye State

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