Drowsy Driver

Figure: Instance of the Drowsiness Detection System.

Drowsiness Detection System

The Drowsiness Detection System that I am proposing is designed to monitor drivers for signs of fatigue and drowsiness in real-time using computer vision and neural networks. By analyzing key facial features such as eye movements and head positioning, the system detects signs of reduced alertness and triggers alerts to prevent potential accidents.

This project is exciting because it addresses a critical safety issue while offering a hands-on application of neural networks, computer vision, and machine learning. It combines cutting-edge technologies, including TensorFlow, OpenCV, and cloud services, to deliver an impactful solution. Beyond technical development, the project provides educational value by enabling experimentation with real-time systems and deployment strategies.

Success will be measured by the system's ability to achieve at least 85% accuracy in classifying drowsiness, its reliability in real-time use, and the successful integration of alert mechanisms like sound alarms and SMS notifications.

Goals

Develop a real-time facial detection pipeline using OpenCV and Dlib.
Train a neural network model (CNN, ANN, or LSTM) for drowsiness classification.
Achieve an accuracy of 85% or higher in drowsiness detection.
Integrate a real-time alert system (buzzer, SMS via Twilio, etc.).
Test the model with real-world driver behavior datasets.

Technology Stack

The project will leverage the following technologies:

Machine Learning: TensorFlow, Keras, PyTorch, scikit-learn
Computer Vision: OpenCV, Dlib, Haar Cascades
Real-Time Processing: Raspberry Pi Camera, Webcam
Deployment: AWS Lambda, EC2, Google Cloud
Notifications: Twilio (SMS), AWS SES (Email)

Success Criteria

The project will be considered successful if:

The model achieves 85%+ accuracy on a real-world dataset.
The system works in real-time with minimal false alarms.
The alert mechanisms successfully notify drivers when drowsiness is detected.

Potential Challenges

Acquiring high-quality labeled datasets for drowsy and alert drivers.
Minimizing false positives to avoid unnecessary alerts.
Ensuring real-time performance on resource-limited devices.