Driver Drowsiness Detection system with Python and Computer Vision

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Driver Drowsiness Detection system with Python and Computer Vision

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1.Abstract

Road accidents caused by driver distraction have become one of the major concerns in modern transportation systems. Driver negligence such as using mobile phones while driving, talking to passengers, eating, drinking, sleeping, or losing focus can lead to severe accidents, injuries, and loss of human life. In public transportation systems, school buses, taxis, and private vehicles, passenger safety largely depends on the driver’s attentiveness and responsible behavior. Therefore, continuous monitoring of driver activities is essential to reduce accidents and improve road safety. To address this issue, the proposed project presents an AI-Based Driver Distraction Monitoring System that uses Artificial Intelligence, Deep Learning, and Computer Vision techniques to automatically detect distracted driving behaviors in real time.

2. Objectives

The main objective of the system is to monitor driver activities continuously and identify dangerous behaviors before accidents occur. The system analyzes driver images and video frames captured through cameras installed inside vehicles and classifies driver activities into multiple categories. A dataset containing ten different driver activity classes was collected from Kaggle for training the deep learning model. The dataset includes activities such as safe driving, texting, talking on the phone, drinking, reaching behind, adjusting controls, hair and makeup activities, and sleeping or drowsiness-related actions. The collected images were preprocessed and augmented to improve model performance under different lighting conditions and viewing angles.

For activity classification, the project uses the ResNet-50 deep learning architecture, which is a powerful Convolutional Neural Network (CNN) model widely used for image classification tasks. ResNet-50 uses residual learning techniques that allow deep neural networks to train efficiently while avoiding problems such as vanishing gradients. The model was trained from scratch using the collected driver activity dataset to accurately recognize distracted driving behaviors. During training, the network learns important visual patterns such as hand movements, facial orientation, eye status, and body posture associated with each activity class.

3.Existing System

• Traditional driver monitoring systems mainly depend on manual observation and basic surveillance methods to monitor driver behavior.

• Some existing systems use simple image processing and sensor-based techniques to detect driver drowsiness and distractions.

• AI-based systems use machine learning and computer vision techniques to identify distracted driving activities such as mobile phone usage and inattentive driving.

• Existing monitoring systems help improve road safety by tracking driver activities and generating alerts during unsafe driving conditions.

Limitations:

Not always accurate or reliable.
Expensive hardware-based solutions.
Intrusive or uncomfortable (wearables).
Limited real-time responsiveness.

4.Proposed System

• The proposed system uses Artificial Intelligence, Deep Learning, and Computer Vision techniques for automatic driver distraction detection.

• The system is trained using a dataset containing ten different driver activity classes collected from Kaggle.

• ResNet-50 architecture is used for accurate driver activity classification and feature extraction.

• The system can detect activities such as mobile phone usage, drowsiness, talking, eating, and inattentive driving.

• A Tkinter-based graphical user interface is developed for real-time monitoring and user interaction.

• The system continuously monitors driver behavior through webcam or surveillance camera feeds and helps prevent accidents by generating alerts during distracted driving situations.

5. Implementation Procedure

Module 1: Face Detection

Capture live video from webcam.
Detect face using OpenCV Haar Cascades or Dlib.

Module 2: Facial Landmark Detection

Identify key facial points (eyes, nose, mouth).
Use Dlib’s 68-point facial landmark detector.

Module 3: Drowsiness Detection Logic

Calculate Eye Aspect Ratio (EAR).
Detect prolonged eye closure.
Monitor yawning using mouth opening ratio.
Count yawning frequency.

Module 4: Real-Time Processing

Continuously process video frames.
Analyze facial features frame by frame.

Module 5: Alert Generation

Trigger alarm if eyes are closed beyond threshold.
Alert on excessive yawning.

Module 6: GUI Development

Build interface using Tkinter.
Display live camera feed and status alerts.

6.Software Requirements

Operating System : Windows 10 64-bit

Programming Language : Python

Front End : Tkinter GUI

Back End : MySQL

Framework : TensorFlow / Keras

Libraries : OpenCV, NumPy, Pandas

7.Hardware Requirements

Processor : Intel i3 or Above

RAM : 4 GB Minimum

Hard Disk : 500 GB

Camera : Webcam / CCTV Camera

System Type : 64-bit Computer System

8. Advantages of the Project

Helps prevent accidents caused by drowsy driving.
Real-time monitoring and alert system.
Non-intrusive (no wearable devices required).
Cost-effective compared to hardware solutions.
Easy to use with GUI interface.
Can be integrated into vehicles for safety systems.

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