A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.

1. DROWSINESS STATE DETECTION OF
DRIVER USING EYELID MOVEMENT
Submitted by,
C.Vignesh
S.Sathya Prakash
G.Udhayakumar

2. OBJECTIVE:
To reduce the road accidents due to sluggishness of driver.
To detect the drivers drowsiness by using eyelid movement with high
accuracy.
To provide a low cost module for alerting the driver while he fells
asleep.

3. ABSTRACT:
Drowsiness detection system is regarded as an effective tool to
reduce the number of road accidents.
This project proposes a non-intrusive approach for detecting
drowsiness in drivers, using Computer Vision.
This system tracks the eye for drowsiness.
The algorithm used is Haar.
This system renders an efficient solution to road accidents and the
cost of developing it into a real time system is also feasible when
compared to the cost involved in the manufacture of an autonomous
car.

4. COMPONENTS DESCRIPTION:
HARDWARE:
Raspberry pi 3
Night vision camera
Buzzer
SOFTWARE:
Raspbian Stretch
OpenCV

5. PROPOSED METHODOLOGY:
The algorithm is coded on OpenCV platform in Linux environment.
The parameters considered to detect drowsiness are face and eye
detection, blinking, eye closure and gaze.
Input is recorded and live fed from a camera that supports night
vision as well.
The algorithm is Haar, trained to detect the face and the eye from the
incoming frame.

6. The core basis for Haar classifier object detection is the Haar-like
features.
These features use the change in contrast values between adjacent
rectangular groups of pixels instead of the intensity values of a pixel.
Once the eye is detected, further coding is done to track the eye and
automatically set a dynamic threshold value.
Depending on the values obtained from each of the incoming frames
and deviations from the threshold values, eyelid closure/blink/gaze is
detected.
Warning system is designed to alert the driver.

7. BLOCK DIAGRAM:
Video Source Face Detection
Drowsiness
Detection
Eye Tracking
Alarm
OR
HMI
Eye Detection

8. SYSTEM OVERVIEW

9. LITERATURE SURVEY:
S.NO TITLE AUTHOR YEAR JOURNAL
1 Camera-based Drowsiness Reference For
Driver State Classification Under Real
Driving Conditions
Fabian Friedrichs and Bin
Yang
2010 IEEE Intelligent Vehicles
Symposium University of
California
2 A Partial Least Squares Regression-based
Fusion Model For Predicting The Trend In
Drowsiness
Hong Su and Gangtie
Zheng
2008 IEEE
3 Driver Drowsiness Detection System Under
Infrared Illumination For An Intelligent
Vehicle
M.J. Flores J. Ma
Armingol A. de la
Escalera
2011 IET Intelligent Transport
Systems
4 Driver Drowsiness Recognition Based On
Computer Vision Technology
Zhang, Wei, Cheng, Bo,
Lin, Yingzi
2012 Tsinghua Science and
Technology
5 Visual Analysis Of Eye State And Head Pose
For Driver Alertness Monitoring
Ralph Oyini Mbouna,
Seong G. Kong
2013 IEEE Transactions On
Intelligent Transportation
Systems

10. CONCLUSION:
The use of object detection and image processing in OpenCV for the
implementation of our proposed work proved to be practically successful.
Driver’s face and eyes are recognized efficiently using OpenCV.
Driver’s eyelid movement is tracked and based on the values drowsiness is
detected accurately.
The eyelid movement is tracked with high accuracy.
Based on the values the driver is alerted while he fells asleep.

11. REFERENCE:
Hong Su and Gangtie Zheng, “A Partial Least Squares Regression-Based Fusion Model for
Predicting the Trend in Drowsiness” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND
CYBERNETICS—PART A: SYSTEMS AND HUMANS, VOL. 38, NO. 5, SEPTEMBER 2008.
Fabian Friedrichs and Bin Yang, “Camera-based Drowsiness Reference for Driver State
Classification under Real Driving Conditions” 2010 IEEE Intelligent Vehicles Symposium
University of California, San Diego, CA, USA June 21-24, 2010.
M.J. Flores J. Ma Armingol A. de la Escalera, “Driver drowsiness detection system under infrared
illumination for an intelligent vehicle” Published in IET Intelligent Transport Systems Received
on 13th October 2009 Revised on 1st April 2011.
Zhang, Wei; Cheng, Bo; Lin, Yingzi,” Driver drowsiness recognition based on computer vision
technology.” Published in: Tsinghua Science and Technology (Volume: 17, Issue: 3) Page(s):354 -
362 Date of Publication: June 2012
International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 4,
Jul-Aug 2015

12. Ralph Oyini Mbouna, Seong G. Kong, Senior Member, IEEE, and Myung-Geun
Chun,” Visual Analysis of Eye State and Head Pose for Driver Alertness
Monitoring.” IEEE TRANSACTIONS ON INTELLIGENT
TRANSPORTATION SYSTEMS, VOL. 14, NO. 3, SEPTEMBER 2013.
Eyosiyas Tadesse, Weihua Sheng, Meiqin Liu,” Driver Drowsiness Detection
through HMM based Dynamic Modeling.” 2014 IEEE International Conference
on Robotics & Automation (ICRA) Hong Kong Convention and Exhibition
Center May 31 - June 7, 2014. Hong Kong, China.
Gustavo A. Peláez C., Fernando García, Arturo de la Escalera, and José María
Armingol,” Driver Monitoring Based on Low-Cost 3-D Sensors.” IEEE
TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL.
15, NO. 4, Page(s): 1855 - 1860 AUGUST 2014.

13. THANK YOU