Drowsiness-Detection

This project focuses on creating a tool for detecting drowsiness in individuals, especially applicable in scenarios like driving where alertness is crucial.

Demo

Overview

Drowsiness Detection uses computer vision techniques to analyze facial features and identify signs of drowsiness in real-time. By monitoring key facial expressions and eye movements, the system can alert individuals when they show signs of drowsiness, potentially preventing accidents caused by fatigue.

How it Works

The detection process involves:

• Facial Landmark Detection: The system identifies facial landmarks, including eyes and mouth, using computer vision algorithms.

• Eye Aspect Ratio (EAR): The EAR is calculated based on the ratio of distances between different facial landmarks. It provides insights into the openness of the eyes.

• Drowsiness Threshold: A predefined threshold is set to determine when an individual is becoming drowsy based on the calculated EAR.

• Alert System: When drowsiness is detected, an alert is triggered, such as a sound alert or a visual notification, to notify the individual to stay alert.

Installation

1. Clone the repository:

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

2. Install the required dependencies:

pip install -r requirements.txt

3. Run the Drowsiness Detection tool:

python drowsiness_detection.py

Usage

1. Ensure your webcam is connected and properly configured.
2. Run the Drowsiness Detection tool.
3. Monitor the real-time output for alerts and notifications when drowsiness is detected.

License

This project is licensed under the terms of the MIT license.