The document presents a seminar on driver drowsiness detection. It discusses the increasing problem of accidents due to drowsy driving and outlines the objectives of building a system to detect driver drowsiness in real-time through monitoring eye blinks and alerting the driver. The proposed methodology uses a behavioral approach including eye detection, blink counting, and analysis to determine drowsiness levels and provide alerts or vehicle control interventions if needed.

1. Pranav Mandani
Driver drowsiness detection
Presented by
Modern Education Society college of Engineering
A seminar on

2. Content
• Introduction
• Motivation
• Problem Statement
• Objective
• Literature Survey
• Methodology
• Conclusion
• References

3. Introduction
• Driver drowsiness detection is a car safety technology which helps prevent accidents caused by the
driver getting drowsy
• Drowsiness is the state of feeling tired or sleepy.
• These happens on most factors if the driver drowsy or if it is alcoholic.
• The number of accidents as a result of drowsiness is increasing day by day.
• Recent statistics estimate that annually 76,000 injuries and 1200 deaths can be attributed to
drowsiness related crashes.
• The advancement of technology in detecting the drowsiness of the driver is a noteworthy challenge
as it can help reduce the probability of accidents taking place resulting in decrease in the death and
injuries caused due to drowsy driving.
• Detection in real-time is the major challenge in the field of accident prevention system.

4. Motivation
• There is a Huge increase in private transportation day by day in today’s world.
• It is tedious or bored to drive for a long period of time.
• Tired driver can get drowsy while driving. Every fraction of seconds drowsiness can turn into
dangerous and life-threatening accidents may lead to death also.
• To prevent this type of incidents, it is required to monitor driver’s alertness continuously and
when it detects drowsiness, the driver should be alerted.
• Through this we can reduce significant number of accidents and can save lives of people.

5. To build a system that detect
driver drowsiness
Problem
Statment

6. Application
reduce the number of
crashes related to
drowsy driving
providing real-time
drowsiness
feedback to the
driver
designed for embedded
systems such as
Android mobile
Accidents Prevention
system

7. The main objective of this project is to ensure the safety system. For
enhancing the safety, we are detecting the eye blinks of the driver and
estimating the driver status and control the car accordingly. On the
whole, by using blinks we can decide if the eye blinks are less, then the
driver is very sleepy and alarm will raised and at the same time and a
message will be sent to the driver . Finally car will be slow down.
Objective

8. Literature Survey
This survey is done to comprehend the need and prerequisite of the general population, and to do
as such, we went through different sites and applications and looked for the fundamental data.
Based on these data, that helped us get new thoughts and make different arrangements for our
task. We reached the decision that there is a need of such application According to the experts it
has been observed that when the drivers do not take break they tend to run a high risk of
becoming drowsy. Study shows that accidents occur due to sleepy drivers in need of a rest, which
means that road accidents occurs more due to drowsiness rather than drink-driving. Attention
assist can warn of inattentiveness and drowsiness in an extended speed range and notify drivers of
their current state of fatigue and the driving time since the last break, offers adjustable sensitivity
and, if a warning is emitted, indicates nearby service areas in the COMAND navigation system

9. Sr. No Title Method Keywords Description Accuracy
1. Driver Drowsiness
Detection System Based
on Visual Features
PERCLOS, Haar
algoithm
Raspberry pi, Eye
Detection, Blink
Count, Image
processing
detection framework is based
on shape predictor algorithm,
that detects the eyes, and
also counts the eye blink rate
followed by drowsiness
detection at real time.
80%
2. Used binary method to
detect the eye state
Convert RGB image
to Grayscale
Convert Grayscale
to Binary
Image Conversion Used binary method to
detect the eye state
-
Literature Review

10. Sr. No Title Method Keywords Description Accuracy
3. Driver fatigue detection
based on eye state
recognition
AdaBoost, LBF and
PERCLOS
Raspberry pi, Eye
Detection, Blink
Count, Image
processing
detection framework is based
on shape predictor algorithm,
that detects the eyes, and
also counts the eye blink rate
followed by drowsiness
detection at real time.
80%
4. Driver Detection system
using Percentage Eyes
closure(PERCLOS)
PERCLOS Drowsiness,PERCLOS,
Viola-Johans
Segmenting
PERCLOS, is the most
effective method for
drowsiness detection,
analyzes drowsiness level of
the driver by using eye states.
In this study, real time eye
detection under infrared
illumination algorithm is
developed for PERCLOS
calculation.
90%

11. Sr. No Title Method Keywords Description Accuracy
5. Driver Drowsiness
Detection Based on Time
Series Analysis of
Steering Wheel Angular
Velocity
Temporal detection
window
Steering wheel
behavior
Uses novel approach of time
series analysis of steering
wheel angular velocity to
detect drowsiness
-
6. Detecting driver
drowsiness using
wireless wearable's
FFT, PSD,
Neural network
Classification
Heart rate, breathing
rate, RR interval,Bio
harness Sensor
Works in two phases: design
wearable Bioharness sensor
to detect biological
parameters of driver and
mobile based drowsiness
detection system is designed.
-

12. Methodology
There are there are 3 approach
• Vehicle -based : vehicle’s environment including changes in speed,
steering wheel movement, etc.
• Physiological approach : Brain activity and heart rate are observed and
then processed
• Behavioral Approach : driver's focus on his driving, by observing the
driver’s head movements, eye, yawning, or facial expression, etc.

13. Behavioral-Approach
• Eye Detection
Step 1 – Take image as input from a camera.
Step 2 – Detect the face in the image and create a Region of Interest (ROI).
Step 3 – Detect the eyes from ROI and feed it to the classifier.
Step 4 – Classifier will categorize whether eyes are open or closed.
Step 5 – Calculate score to check whether the person is drowsy.
Step 6 – If the level of fatigue is more then give an alert to the driver by ringing
the alarm and visual warning on a navigation display.
STEP 7 – the alarm sounds until the driver wakes up
Stop

14. Eyes Analysis

15. Workflow -
Input Image
Image
Processing
ROI Eye Detection
Creating
Bounding Box
Recognisation
Tracking
Eye Detection

16. Behavioral-Approach
STEP 1 – Take image as input from a camera.
STEP 2 – Detect the face in the image and capture to extract frames one by one .
STEP 3 – Detect the eyes region and mouth region.
STEP 4 – Each extracted frame is analyzed at time to study the pattern of facial
features.
STEP 5 – Calculate EAR and MAR for each frame .
STEP 6 – If the values exceed the threshold then a blink and yawn is considered.
STEP 7 – if eye blinking rate and yawns are suspected for a certain number of
consecutive frames
STEP 8 – Then alarm keeps on ringing until the driver wakes up
•Analysis eye blinking and yawn detection :

18. Conclusion
In order for the system to detect sleepiness successfully, a set of parameters need to be given to the
system manually, and it detects the drowsiness based on values that are being calculated
Thus with 3 measures or approaches we can detect driver’s drowsiness , where we seen the behavioral
approach , as it cost low.
The work is based on behavior analysis, high end camera installation and conventional algorithm to detect
the possible coordinate to identify eyes and mouth.
Wearing glasses (of any kind) cause the system to fail.
. In future, wearable device should be proposed in order to have more accuracy and efficiency for
detecting the drowsiness and fatigue of the driver to minimize the rate of road accidents.

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