Project Report
Submitted in partial fulfillment of the requirements for award
of Bachelor of Computer Applications
SCHOOL OF COMPUTER APPLICATIONS
SECTION -A
Submitted to : Submitted by
Mr. Amit Jha Sir Ishant Somani
21BCAN303
Content:
1. Introduction
2. Objective
3. The Problem: Drowsiness and its Impact
4. Our Solution: A Drowsiness Detection System with OpenCV, dlib, and
Python
5. Data Acquisition
6. System Development with OpenCV and dlib
7. Flow Diagram
8. Project Demonstration
9. Future Scope
10. Limitation
11. Conclusion
Introduction
The attention position of driver degrades because of lower sleep, long nonstop driving
or any other medical condition like brain diseases etc. Several checks on road
accidents says that around 30 percent of accidents are caused by fatigue of the
motorist. When driver drives for further than normal period for mortal also inordinate
fatigue is caused and also results in frazzle which drives the motorist to sleepy
condition or loss of knowledge. Drowsiness is a complex miracle which states that
there's a drop in cautions and conscious situations of the driver. Though there's no
direct measure to descry the drowsiness but several other indirect approaches can be
used for this purpose.
Objective
• Driver drowsiness detection is a car safety technology which helps to save the life of the
driver by preventing accidents when the driver is getting drowsy.
• The main objective is to first design a system to detect driver's drowsiness by continuously
monitoring retina of the eye.
• The system works in spite of driver wearing spectacles and in various lighting conditions.
• To alert the driver on the detection of drowsiness by using buzzer or alarm.
• Speed of the vehicle can be reduced.
• Traffic management can be maintained by reducing the accidents
The Problem: Drowsiness and its Impact
• Drowsy driving is a state of impaired alertness similar to intoxication.
• Statistics reveal a worrying trend: drowsy driving is a contributing factor in a significant
portion of road accidents.
• The effects of fatigue behind the wheel can lead to slower reaction times, impaired
judgment, and an increased risk of crashes.
Our Solution: A Drowsiness Detection System
with OpenCV, dlib, and Python
• This project proposes a drowsiness detection system leveraging the power of OpenCV, dlib, and
Python.
• OpenCV (Open Source Computer Vision Library) is a library for real-time computer vision tasks.
• DLIB is a Python library with powerful tools for image processing and facial landmark detection.
• Python provides a versatile and user-friendly programming language for data science
applications.
• The system will employ facial landmark detection, eye closure analysis, and potentially head
pose estimation to identify signs of fatigue.
Data Acquisition
• We won't require a massive dataset for this project.
• We can gather data using a webcam, recording short videos of participants
under controlled conditions, simulating driving scenarios.
• Ethical considerations regarding data collection and participant privacy will
be strictly adhered to.
System Development with OpenCV and dlib
• OpenCV will be used to:
○ Access the webcam and capture video frames.
○ Convert frames to grayscale for efficient processing.
• DLIB will be employed to:
○ Load pre-trained models for face and eye detection.
○ Detect faces and facial landmarks (eyes, mouth) in each frame.
• Python will be used to:
○ Write the core logic of the system.
○ Implement algorithms to analyze facial landmarks and identify drowsiness.
Flow Diagram
Project Demonstration
ACTIVE
Project Demonstration
DROWSY(Alert with alarm)
Future scope
• The model can be improved incrementally by using other parameter blinking rate,
state of the cars, etc.
• If all these parameter are used it can improve the accuracy by lot.
• We plan to future work on the project by adding a sensor to o track the heart rate in
order to prevent the accident caused due to sudden heart attack to driver.
• Same model and techniques can be used for various other uses like Netflix and other
streaming services can detect when is asleep and stop the video accordingly.
• It can also be used in application that prevent user from sleeping
Limitation
• Optimum range required when the separation among face and webcam isn't at ideal range then
certain issues are emerging.
• At the point when face is away from the webcam (more than 70cm) at that point the backlight is
deficient to light up the face appropriately. So eyes are not identified with high exactness which
shows mistake in detection of laziness.
• Orientation of face
• Dependence on ambient light
• Problem with multiple faces
• Delay in sounding alarm
Conclusion
• The driver anomaly observing framework created is able of identifying laziness, intoxicated and
careless practices of driver in a brief time. The Laziness Detecting Framework created based on
eye closure of the driver can separate ordinary eye flicker and tiredness and distinguish the
laziness while driving.
• The suggested device is able to avoid the incidents when driving due to sleepiness. The system
works properly even in case of drivers sporting spectacles and even below low light stipulations if
the digital camera offers higher output.
• Information about the head and eyes position is obtained through a range of self-developed
photograph processing algorithms. During the monitoring, the system is able to figure out if the
eyes are opened or closed. When the eyes have been closed for too long, a warning sign is issued.
• Processing judges the driver's alertness level on the groundwork of continuous eye closures
Thank You
