Driver Drowsiness Detection System using machine learning ppt.

Driver
Drowsiness
Detection
System
PRESENTER NAME:
1. CHHAVI DABAS

Abstract
It occurs when a driver is so fatigued or sleep-deprived that they are unable to maintain their full attention and
alertness while driving. Drowsy driving is a problem that affects drivers of all ages and can occur at any time of
day. There are many factors that can contribute to drowsy driving, including lack of sleep, and long driving hours,
shift work, and the use of certain medications. Drowsy driving can also be exacerbated by factors such as alcohol
or drug use, or underlying medical conditions such as sleep disorders.
The consequences of drowsy driving can be severe, with estimates suggesting that it may be a factor in up to 40%
of motor vehicle accidents. To address this issue, we aimed to develop a system for detecting and preventing
drowsy driving in real-time by using a combination of hardware and software solutions. On the hardware side, we
used a camera to collect data about the driver's behavior and physical state. This included measures such as eye
movements, facial expressions, and distance from the camera. On the software side, we developed algorithms to
analyze this data in real-time and identify patterns that are indicative of drowsy driving. When drowsy driving is
detected, the system can take various actions to alert the driver and prevent an accident from occurring. These
actions include sounding an alarm and displaying a warning message. This can prevent an accident from occurring.
Drowsy driving is a serious safety concern that can lead to accidents, injuries, and fatalities on the road.

Introduction
Every year, thousands of accidents occur due to drowsy driving, putting lives at risk on the road.
Detecting driver drowsiness is a critical aspect of ensuring road safety. In this research, we delve into
an innovative approach for drowsiness detection that utilizes Eye Aspect Ratio (EAR). Drowsy driving,
a prevalent issue, is often characterized by drivers' reduced alertness and responsiveness. As
drowsiness sets in, individuals tend to exhibit specific changes in their facial expressions, including
partially closed eyes, slightly open mouths, and alterations in their overall face shape. These subtle
but telling transformations provide valuable cues that can be harnessed to create a reliable driver
drowsiness detection system. The importance of such a drowsiness detection system cannot be
overstated. It has the potential to save lives, reduce accidents, and enhance overall road safety. As we
progress in this study, we will delve into the technical details and methodologies employed in utilizing
EAR to create an effective and practical solution for detecting driver drowsiness.

Methodology
Drowsy Driver Detection System has been developed, using the intrusive machine vision-based concepts.
The system uses a web camera that points directly towards the driver’s face and monitors the driver’s eye
movements in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to
alert the driver. The algorithm developed is different from any currently published papers, which was a
primary objective of the project. The system deals with detecting eyes within the specific segment of the
face. If these are not found for 20 consecutive frames, the system draws the conclusion that the driver is
falling asleep. In this project we have developed drowsiness detection system by using Python. The input
video is captured by using webcam (camera) and then it will be extracted. The face and eye detection are
done by using OpenCV. By using the Euclidean eye aspect ratio, we can get eye blinking ratio, it helps to
detect either eyes are open or closed. It will detect the face and eyes of the driver by using the given
commands. Then it will detect whether the eyes of driver are open or close. If the eyes are closed more than
given time interval it will warn the driver by playing the alarm or if eyes are open it will display message
“eyes open” and then it will go to taking the video of driver and the process will go on.

Methodology
1. Data Collection – First a picture is captured
through in vehicle cameras with varying
angles and lighting conditions to simulate
real-world scenarios.
2. Preprocessing – Human face is first divided
into 68 X,Y coordinates to locate key points
on the face, including eyes and mouth. After
that cropping and resizing of facial regions is
done for consistent input to drowsiness
detection model and image is normalized
with respect to lighting and color variations.
68 Face landmarks detected by model

Methodology
3. Feature extraction- Calculate EAR:
The more the EAR, the
more widely eye is open.
We would decide a
minimum EAR value and
used this to decide if the
eye is closed or not.

Methodology
Function that would return the EAR for a single eye.
def eye_aspect_ratio(eye):
p2_minus_p6 = dist.euclidean(eye[1], eye[5])
ear = (p2_minus_p6 + p3_minus_p5) / (2.0 * p1_minus_p4)
return ear
Range Status
EAR <0.25 Drowsy, eyes are closed

Experimental Setup
For the purpose of detection and recognition of face Eigen face algorithm is used further PCA is used to classify it.
For the detection of eyes, we are using dlib 82 landmarks.
System configuration (Hardware Requirement):
RAM: 2 GB Ram
SIZE: 300 MB of Free space
CPU: Intel Pentium Inside
GPU: Intel 82945G Express
Software Requirement: No special software is required to run the application

Conclusion
In this article, we provide a method for automatically classifying blink types which includes
establishing a threshold based on the Eye Aspect Ratio value. The model can be effective to
protect road accidents.

Driver Drowsiness Detection System using machine learning ppt.

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