3. Introduction[1][3]
➢At present, the automobile is an essential
mode of transportation for people.
➢Although the automobile has changed
people’s lifestyle and improved the
convenience of conducting daily activities,
it is also associated with numerous negative
effects, such as traffic accidents.
➢Drowsiness Detection is the detection of a
person to check whether the person is
feeling sleepy while performing a
significant task.
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4. ➢The detection methods are categorized as subjective and objective
detection.
➢Thus python allows the model of deep learning algorithm via including the
use of OpenCV.
➢A detecting system for vision-based automatic driver drowsiness detection
has been proposed.
➢ In these accidents, fatigue driving caused approximately 20% – 30%
traffic accidents
Introduction[1]
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5. Motivation[1][2]
➢Based on 2017 police and hospital reports, the National Highway Traffic Safety
Administration (NHTSA) identified 91,000 car accidents as being caused by drowsy
drivers.
➢These accidents resulted in 50,000 injuries. In 2019, 697 fatalities involved a drowsy
driver.
➢However, NHTSA admits that it is hard to determine the precise number of drowsy-
driving accidents, injuries, or deaths and that the reported numbers are underestimates
➢For example, a study by the American Automobile Association’s foundation for
traffic safety estimated that more than 320,000 drowsy driving accidents happen each
year, including 6400 fatal crashes .
➢The high numbers indicate that drowsy driving is a serious concern that needs to be
addressed to mitigate its impact.
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6. Objectives[5]
Following are some of the objectives of this android based drowsiness
detection system:
➢To detect driver’s drowsiness by continuously monitoring retina of the eye.
➢To research on the various system to detect drowsiness.
➢To implementation of an accurate algorithm for the motion of eye detection .
➢To prepare report based on the project.
➢To aid in the prevention of accidents passenger and commercial
vehicles.
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7. Scope of the Project[2]
➢An application can be developed where it can alert or prevent the user
from sleeping.
➢The system can be made more accurate using various other parameters
such as State of the Car, Detecting Foreign Substances on Face etc.
➢Similar models and techniques can be used for various other uses such
as Netflix, Hotstar and other streaming service platforms can detect
whether the person is sleeping and stop the video accordingly.
➢If the child sleeps while studying his/her parents can get the message.
➢In future we can implement drowsiness detection system in aircraft in
order to alert piolet
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8. Limitations of the project[1]
➢While researching and building a project, fatigue is observed using
image recognition.
➢Keeping the user’s head down causes fatigue detection inaccuracy.
➢Camera motion.
➢The eye-detection algorithm which plays an important role in detecting
drowsiness creates a high degree of misunderstanding when tested with
different positions of eyes.
➢Lightning conditions.
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10. Literature Review[5]
Sr.No Title Authors Year of publication
[1] Real Time Driver
Drowsiness Detection
Srihitha Jujhavarapu 2017
[2] Driver Drowsiness
Detection
Čolić, Aleksandar, Oge
Marques, and Borko
Furht.
2014
[3] Driver Drowsiness
Detection Using Eye-
Closeness Detection
Chotchinasri, Varakorn
Koschakosai, and
Narit Hnoohom
2016
[4] Driver Distraction and
Drowsiness Detection
System."
Roshini, G., Y. Kavya,
R. Hareesh, M. Suma,
and N. Sunny
2021
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11. Overview Of Proposed System[2]
➢The proposed system is designed for daily use
➢The proposed system is used to detect the drowsiness of users while
driving
➢The proposed system can be used for the people who wear glasses
➢The proposed may lack incase of anti-light and dark
➢The proposed can be used with no internet connection
➢The proposed system can be used cost-free
➢The proposed system is easy to use and straight forward
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13. Methodology of System[6]
Algorithm:
➢Step 1 – Access the camera and take image as input from the camera.
➢Step 2 – Detect the face in the image and create a Region of Interest
(ROI).
➢Step 3 – Detect the eyes from ROI and Mark the eye points.
➢Step 4 – Calculate the aspect ratio for the left and right eyes and set
the criteria for the closing of eyes (drowsiness detection).
➢Step 5 — Describe the audio and text message for the user to alert
them of drowsiness.
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14. Methodology of System[6]
Eye Aspect Ratio (EAR) :
➢The detection of drowsiness of the driver is based on eye blink rate.
➢The Eye Aspect Ratio (EAR) formula, is used to detect the eye blinking.
➢If driver blinks eyes more frequently, it means that the driver is in the state
of drowsiness.
➢Thus, it is necessary to detect the eyes shape accurately in order to
calculate the eye blink frequency.
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15. Methodology of System[6]
➢Eye Aspect Ratio (EAR) :
➢The formula for calculating EAR is given by:
➢The p2, p3, p5 and p6 are used to measure the height whereas p1 and p4 are
used to measure width of the eyes in meter (m).
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17. Conclusion
The aim of this study is to address a solution to one of the major causes of
the road accident, the driver drowsiness; the proposed solution does track the
driver’s eyes and then notify him when his eyes get closed in order to avoid
losing the control of the car and causing traffic accidents.
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