Driver Monitoring System

Overview

The Driver Monitoring System is a real-time AI-based application that detects driver drowsiness and yawning using computer vision techniques. It leverages OpenCV, Dlib, and Flask to process video feeds and provide visual and auditory alerts when signs of fatigue are detected.

Features

• Eye Aspect Ratio (EAR) Calculation: Detects drowsiness by monitoring the eye-blinking rate.
• Lip Distance Measurement: Identifies yawning based on mouth opening.
• Live Video Streaming: Integrates with Flask for real-time web-based monitoring.
• Alert System: Displays visual alerts when drowsiness or yawning is detected.

Installation

Prerequisites

Ensure you have the following dependencies installed:

• Python 3.8+
• OpenCV
• Dlib
• imutils
• Flask
• NumPy
• SciPy

Setup

1. Clone the repository:

   git clone https://github.com/your-repo/driver-monitoring.git
   cd driver-monitoring

2. Install dependencies:

   pip install -r requirements.txt

3. Download the required dlib facial landmark model:

   wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
   bzip2 -d shape_predictor_68_face_landmarks.dat.bz2

Usage

1. Start the Flask server:

   python app.py

2. Open a browser and visit:

   http://127.0.0.1:5000/
   

3. The web interface will display the live video feed and issue alerts if drowsiness or yawning is detected.

Configuration

Modify thresholds in config.py:

• EYE_AR_THRESH (default: 0.27): Eye aspect ratio threshold for drowsiness detection.
• EYE_AR_CONSEC_FRAMES (default: 17): Number of frames before drowsiness alert.
• YAWN_THRESH (default: 20): Lip distance threshold for yawning detection.
• YAWN_CONSEC_FRAMES (default: 7): Number of frames before yawning alert.

Required Files & Dependencies

1. Files & Directory Structure

/driver-monitoring
│── app.py                # Main application script
│── requirements.txt       # List of dependencies
│── shape_predictor_68_face_landmarks.dat  # Dlib model
│── templates/
│   └── index.html         # Web interface template
│── static/
│   └── style.css          # Stylesheet (optional)
│── config.py              # Configuration parameters
│── README.md              # Project Documentation

2. Required Dependencies

Install dependencies using:

pip install -r requirements.txt

Or install manually:

pip install opencv-python dlib imutils flask numpy scipy

3. Download Dlib Model

Download the 68-face landmark model:

wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
bzip2 -d shape_predictor_68_face_landmarks.dat.bz2

Place this file in the project directory.

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How to Run the Project

1. Clone the Repository

git clone https://github.com/your-repo/driver-monitoring.git
cd driver-monitoring

2. Run the Application

python app.py

3. Access the Web Interface

Open your browser and go to:

http://127.0.0.1:5000/

---

Configuration

Modify config.py to adjust thresholds:

class Config:
    EYE_AR_THRESH = 0.27  # Eye Aspect Ratio Threshold
    EYE_AR_CONSEC_FRAMES = 17  # Consecutive Frames for Drowsiness
    YAWN_THRESH = 20  # Lip Distance Threshold for Yawning
    YAWN_CONSEC_FRAMES = 7  # Consecutive Frames for Yawning

Troubleshooting

• No Video Feed? Ensure your webcam is connected.
• Dlib Model Not Found? Download and place shape_predictor_68_face_landmarks.dat in the project folder.
• Errors in Flask? Ensure Flask is installed and running on Python 3.8+.

Future Improvements

• Sound Alerts for drowsiness detection.
• Mobile App Integration for remote monitoring.
• Better Performance Optimization for real-time processing.

Acknowledgments

• OpenCV for real-time video processing.
• Dlib for facial landmark detection.
• SciPy for Euclidean distance calculation.