DROWSINESS final_viva in health care .pptx

School of Computer Science and Engineering 1
SRM INSTITUTE OF SCIENCE AND TECHNOLOGY
Ramapuram, Chennai – 600 089
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
18CSP109L- MAJOR PROJECT
Enhancing Driver Safety Through Transfer Learning Models for Detecting
Drowsiness Based on Eye Movements
BATCH NUMBER: 05
Team Members Supervisor
N SIVA SAI DANUSH [RA2111003020129]
MEDURI NIKHESH [RA2111003020162]
Y V SURYA PRAKASH [RA2111003020173]
NAME: Mrs.S.Sajini, AP/CSE
12-05-2025

AGENDA
• Abstract
• Scope and Motivation
• Introduction
• Literature Survey ( Table format-year should be in Chronological order)
• Objectives
• Problem Statement
• Proposed Work
• Architecture Diagram/Flow Diagram/Block Diagram
• Novel idea
• Modules
• Module Description
• Software & Hardware Requirements
• Implementation
• Results and Discussion
• References (Base paper hard copy to be submitted to the supervisor.)
• Way forward towards Outcome (Research Paper/Patent)
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ABSTRACT
• Driver fatigue causes many road accidents, and traditional detection methods like EEG and costly sensors are not practical for daily
use. The project uses Haar Cascade classifiers to track eye movements and identify drowsiness through the Eye Aspect Ratio
(EAR).Real-time video is used to analyze facial and eye landmarks, calculate EAR, and detect drowsiness using machine learning
models. Alerts are generated immediately when drowsiness is detected to prevent accidents. The system is designed to be
affordable, scalable, and easy to use with regular cameras, improving road safety. It can also be integrated into existing driver-
assistance systems for wider applications. Future versions could support more advanced hardware for higher accuracy. Enhancing
Driver Safety Through Transfer Learning Models for Detecting Drowsiness Based on Eye Movements
12-05-2025

SCOPE AND MOTIVATION
• This project aims to create a real-time system to monitor driver safety by detecting drowsiness using Haar Cascade classifiers and
transfer learning models for more accurate and faster fatigue classification.
• The solution is cost-effective as it works with regular webcams or mobile phone cameras, eliminating the need for specialized
equipment, which keeps costs low and makes the system easily accessible for everyday use, even for individuals or businesses on a
budget.
• Driver fatigue is a leading cause of road accidents worldwide. By monitoring fatigue in a non-invasive, affordable way, this system
can help reduce accidents and save lives..
• Software-based machine learning models make it easier to implement in regular vehicles and integrate with advanced safety
systems, improving road safety in a scalable way. This approach reduces reliance on costly hardware, making it a viable solution for
both existing and new vehicles.
12-05-2025

INTRODUCTION
• This project aims to develop a real-time driver drowsiness detection system using computer vision and machine learning to enhance
road safety. Since driver fatigue is a major cause of accidents, traditional detection methods like EEG sensors are often impractical
due to their high cost and complexity. To address this, we use a camera-based approach that tracks eye movements and detects
drowsiness through Haar Cascade classifiers and Eye Aspect Ratio (EAR) calculations. If prolonged eye closure is detected, the
system triggers real-time alerts (visual or auditory) to warn the driver. To improve accuracy and efficiency, we incorporate transfer
learning models, which reduce the need for extensive training data and optimize real-time performance. Designed to be cost-
effective and easy to implement, the system works with regular webcams or mobile phone cameras and can integrate with Advanced
Driver Assistance Systems (ADAS), fleet management, and smart vehicle technologies. By leveraging AI and deep learning, this
project provides a non-invasive, efficient, and practical solution to monitoring driver fatigue, reducing drowsiness-related accidents,
and improving overall transportation safety.
12-05-2025

LITERATURE SURVEY
S. No Title Authors Year
AI/ML
Technique Description Advantages Limitations Key Findings
1
Real-Time Drowsiness
Detection
Chen et al. 2024
Transfer
Learning, CNN
Multi-modal
driver state
analysis
94% accuracy
Computationa
l complexity
Enhanced
safety
prediction
2
Multimodal
Drowsiness Detection
Framework
Rodriguez et
al.
2023
Multi-sensor
Transfer
Learning
Integrated
driver
behavior
analysis
Comprehensiv
e monitoring
High data
complexity
Robust
drowsiness
identification
3
Deep Learning for
Driver State
Recognition
Thompson &
Lee
2024
Transformer
Transfer
Learning
Advanced
driver
behavioral
prediction
Adaptive
learning
High
computational
requirements
Precise fatigue
assessment
4
Explainable AI for
Driver Drowsiness
Garcia et al. 2023
SHAP,
Interpretable
Models
Transparent
drowsiness
detection
Model
interpretabilit
y
Explanation
complexity
Improved
safety insights
12-05-2025

OBJECTIVES
• To implement a real-time drowsiness detection system using Haar Cascade classifiers and Eye Aspect Ratio (EAR) calculations
This system detects driver drowsiness by analyzing facial and eye landmarks using Haar Cascade classifiers and monitoring Eye Aspect
Ratio (EAR) to track eye closure patterns. By processing real-time video input, it ensures an efficient and non-invasive method for
detecting fatigue.
• To enhance model efficiency using transfer learning for improved accuracy and reduced training time
By integrating transfer learning, the system leverages pre-trained deep learning models to minimize training time while maintaining high
accuracy. This approach makes the detection faster, adaptable, and scalable, allowing it to perform well in different lighting conditions
and user environments.
• To propose a cost-effective, easily deployable solution with real-time alerts for accident prevention
Designed for affordability and accessibility, the system operates using standard webcams or mobile cameras, eliminating the need for
expensive hardware. It generates instant visual or auditory alerts upon detecting drowsiness and can integrate with ADAS, fleet
management, and smart vehicle technologies to enhance road safety.
12-05-2025

PROBLEM STATEMENT
• Road Safety Concern: Drowsy driving is a leading cause of accidents, resulting in injuries and fatalities.
• Lack of Awareness: Drivers may not realize when they are too fatigued to drive safely.
• Delayed Response : Traditional methods of detecting drowsiness rely on self-awareness, which is unreliable.
• Need for Automation: There is a need for an automated system that can detect drowsiness in real-time and alert drivers.
• Preventive Solution: An AI-based detection system can help reduce accidents and improve road safety.
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BLOCK DIAGRAM OF THE PROPOSED SYSTEM
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NOVEL IDEA
The novel idea of this project is to enhance drowsiness detection performance under extreme conditions such as low light, glare, and
facial obstructions from glasses or masks. To address these challenges, adaptive contrast adjustment improves visibility in dark
environments, while semantic segmentation and inpainting techniques reduce glare for more accurate eye tracking. When parts of the
face are obstructed, the system prioritizes detecting upper facial regions to maintain reliable performance.
To optimize real-time detection, lightweight deep learning models like MobileNet and YOLO Nano ensure efficiency on resource-
limited devices. These enhancements significantly improve accuracy, making the system more reliable and adaptable for real-world
applications. With its ability to function effectively in challenging environments, this technology can be integrated into smart vehicles,
fleet management, and healthcare monitoring, providing a scalable and versatile safety solution.
12-05-2025

MODULES
• Data Acquisition Module
• Preprocessing Module
• Face & Eye Detection Module.
• Drowsiness Detection Module.
• Alert Generation Module.
• Integration Module.
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MODULE DESCRIPTION
• Data Acquisition Module: Captures real-time video using webcam or mobile camera.
• Preprocessing Module: Applies contrast enhancement and glare reduction techniques.
• Face & Eye Detection Module: Uses Haar Cascade classifiers to detect facial and eye landmarks.
• Drowsiness Detection Module: Calculates Eye Aspect Ratio (EAR) and classifies drowsiness using a trained model.
• Alert Generation Module: Triggers audio/visual alerts when drowsiness is detected.
• Integration Module: Enables compatibility with ADAS, fleet management, and healthcare systems.
12-05-2025

SOFTWARE & HARDWARE REQUIREMENTS
Software Requirements:
•Programming Language: Python
Libraries Used:
• OpenCV – Image processing and Haar Cascade classifiers
• TensorFlow/Keras – Machine learning models
• dlib – Face and eye landmark detection
• NumPy – Numerical computations
Hardware Requirements:
• Webcam/Mobile Camera – Captures real-time video.
• Computer/Embedded Device – Runs the detection model in real time.
• Speakers/Buzzer (Optional) – Provides audio alerts.
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Implementation
12-05-2025

Module Output1
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Module Output2
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RESULTS AND DISCUSSIONS
The implemented Driver Drowsiness Detection System was evaluated under different real-world scenarios to assess its detection
capability, performance, and efficiency. The system accurately identified signs of fatigue by analyzing facial features—primarily eye
movement—using the Eye Aspect Ratio (EAR) as a core metric. When the EAR consistently dropped below the preset threshold, the
system successfully detected extended eye closure and generated real-time alerts through audio signals or visual warnings.
The system was tested using short and extended driving simulations under varying lighting conditions, including daylight, dim
environments, and artificial lighting. It maintained stable performance, consistently delivering detection results without noticeable
delay. The model operated smoothly at 24 frames per second, ensuring continuous monitoring without interruption. This
responsiveness is critical in real-time driver assistance applications. Precision and recall values reached 91% and 89%, respectively,
reflecting the system’s ability to minimize both false alerts and missed detections. The detection module also performed well despite
minor facial obstructions, such as glasses or partial shadows, which confirms the robustness of the face and eye landmark detection.
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MODEL PERFORMANCE
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COMPARISON TABLE
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CONCLUSION
• The development of the Driver Drowsiness Detection System in this project showcases how computer vision and AI can be effectively
used to enhance driving safety. By observing visual cues such as prolonged eye closure, blinking patterns, and facial orientation, the
system identifies early signs of fatigue with minimal user interaction. This real-time, contactless monitoring approach helps address the
growing issue of drowsy driving, which continues to contribute to many road accidents globally.
• The system operates using real-time video input, processed through OpenCV and analyzed using efficient deep learning models. It
evaluates driver alertness through Eye Aspect Ratio (EAR) calculations and facial landmark detection, ensuring accuracy without the need
for expensive hardware. The inclusion of preprocessing steps—such as noise filtering, contrast improvement, and glare handling—ensures
consistent performance, even in low-light or visually complex conditions.
• Ultimately, the proposed solution delivers a practical, efficient, and scalable way to reduce fatigue- related accidents. Its compatibility
with common hardware, real-time responsiveness, and adaptability to varied driving environments make it well-suited for widespread
deployment. With continued development, it could play a significant role in advancing intelligent vehicle systems and promoting safer
roads through technology.
12-05-2025

FUTURE SCOPE
• To further improve the accuracy and adaptability of the drowsiness detection system, future versions can incorporate additional
physiological inputs such as heart rate monitoring or eye temperature sensors. Combining visual indicators with biometric signals
can help reduce false positives and improve detection in cases where eye movement alone may not fully reflect a driver’s level of
fatigue.
• Another area of improvement involves the integration of advanced deep learning models. While the current system uses lightweight
models for efficiency, implementing more complex neural networks such as Long Short-Term Memory or CNN-RNN hybrids could
allow the system to analyze behavioral patterns over time rather than relying solely on frame-by-frame detection. This temporal
analysis could lead to more accurate fatigue prediction based on driver history and extended behavior tracking.
• Finally, the inclusion of predictive analytics and AI-driven recommendations would make the system not just reactive but also
proactive. By learning from historical data and driving habits, the system could provide personalized suggestions such as advising
rest breaks or adjusting driving schedules. These intelligent insights would not only enhance safety but also promote healthier
driving behaviors over time.
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PROJECT OUTCOME
12-05-2025

APPLICATIONS
• Road Safety: Real-time monitoring of drivers’ alertness to prevent accidents.
• Fleet Management: Monitor commercial vehicle drivers to ensure their safety during long-distance driving.
• Driver Insurance: Used by insurance companies to assess risk and reward safe drivers with discounts.
• Smart Vehicles: Integration with ADAS (Advanced Driver Assistance Systems) for safer autonomous driving.
• Health Monitoring: The technology could be adapted for use in healthcare, monitoring patients with sleep disorders or
fatigue.
• Public Transport Safety: Ensures the alertness of bus and taxi drivers, reducing the risk of passenger-related accidents.
• Railway Operator Monitoring: Detects drowsiness in train drivers and metro operators to prevent rail accidents.
• Aviation Safety: Assists in monitoring pilot fatigue during long-haul flights, enhancing flight safety.
• Construction and Heavy Machinery: Prevents workplace accidents by monitoring fatigue levels in crane and forklift
operators.
• Military and Defense Operations: Helps track the alertness of soldiers and vehicle operators during critical missions.
12-05-2025

REFERENCES
1. H. A. Madni et al., "Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior," IEEE
Access, vol. 12, pp. 64765-64778, May 2024.
2. A. Chen et al., "Real-Time Drowsiness Detection," IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 3, pp.
456-472, Mar. 2024.
3. S. Kumar and R. Singh, "Machine Learning for Driver Fatigue Monitoring," Transportation Research Part C, vol. 152, pp. 112-
129, Feb. 2024.
4. L. Wang et al., "Edge AI in Driver Safety Systems," IEEE Journal of Selected Topics in Signal Processing, vol. 18, no. 2, pp. 256-
271, Feb. 2024.
5. M. Rodriguez et al., "Multimodal Drowsiness Detection Framework," ACM Transactions on Computing for Healthcare, vol. 5, no.
1, pp. 78-95, Jan. 2024.
6. D. Salem and M. Waleed, "Drowsiness Detection in Real-Time via Convolutional Neural Networks and Transfer Learning," Journal
of Engineering and Applied Science, vol. 71, Art. no. 122, May 2024.
7. R. Florez et al., "A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth
Using Convolutional Neural Network and Mouth Aspect Ratio," Sensors, vol. 24, no. 19, Art. no. 6261, Oct. 2024.
8. S. S. Sengar, A. Kumar, and O. Singh, "VigilEye: Artificial Intelligence-Based Real-Time Driver Drowsiness Detection," arXiv
preprint arXiv:2406.15646, Jun. 2024.
9. T. Malpekar and S. Harne, "DrowsiScan: Early Detection of Driver Drowsiness Using Deep Learning," International Journal for
Research in Applied Science and Engineering Technology, vol. 6, no. 6, pp. 31576, Nov. 2024.
12-05-2025

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