Driver Drowsiness Detection

Abstract

1. Introduction

This project implements a drowsiness detection system using a webcam. The system utilizes computer vision techniques to monitor a person's eyes and detect signs of drowsiness in real-time.

2. Objective

This project implements a drowsiness detection system using a webcam. The system utilizes computer vision techniques to monitor a person's eyes and detect signs of drowsiness in real-time.

This project demonstrates how HTML can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/Shashivadan/Driver-Drowsiness-Detection.git
cd Driver-Drowsiness-Detection

1. bash or PowerShell
2. Install the required Python packages. You can use pip to install them:
3. Download the shape predictor model file from the dlib website:
4. Visit: http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
5. Extract the compressed file to the project directory.
6. Rename the extracted file to shape_predictor_68_face_landmarks.dat.
7. Run the Python script main.py:
8. Open a web browser and visit http://localhost:5000.

git clone https://github.com/Shashivadan/drowsiness-detection.git

pip install -r requirements.txt

python main.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by Shashivadan and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.