Drowsiness-detection

The project implements an AI-based drowsiness detection system using
computer vision and machine learning techniques. It leverages a pre-trained
dlib facial landmark predictor to detect and analyze the user's eyes in real-time
through a webcam. The system calculates the Eye Aspect Ratio (EAR) to
monitor eye blinks and determine drowsiness. If the EAR falls below a
defined threshold for a specified number of consecutive frames, an alert is
triggered. This alert is both visual, displayed on the video frame, and auditory,
using the pyttsx3 text-to-speech engine to warn the user. The system is
designed to help prevent accidents by alerting users when they show signs of drowsiness.

Enhance Road Safety: Develop a system that significantly reduces the risk of
accidents caused by driver fatigue by providing real-time monitoring and
alerts. • Accurate Detection: Implement algorithms capable of accurately detecting
early signs of drowsiness using various indicators such as eye movement,
head position, and yawning frequency. • Real-Time Processing: Ensure the system can process data and provide
feedback in real time to enable timely intervention and prevent accidents. • User-Friendly Interface: Design an intuitive and easy-to-use interface for
drivers to interact with the system without causing distraction. • High Reliability and Precision: Achieve high reliability and precision in
drowsiness detection under various driving conditions and for different
drivers. • Integration with Vehicle Systems: Facilitate seamless integration with
existing vehicle systems to leverage data from in-car sensors and cameras. Cost-Effectiveness: Create a cost-effective solution that can be widely
implemented in consumer vehicles without significantly increasing production costs