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Build a real-time drowsiness detection system using Python, OpenCV. Detect closed eyes and yawning to alert sleepy drivers with audio warnings and visual cues.
Beginner-friendly. Certificate included.
Startups
Microsoft
NVIDIA
By the end of this course, you'll have the skills to land your first computer vision job or freelance clients.
Learn how AI identifies signs of driver fatigue through facial cues and eye movement analysis.
Use Python with OpenCV and techniques like EAR (Eye Aspect Ratio) to monitor drowsiness.
Develop a real-time system that detects closed eyes, yawning, and head tilts to alert drowsy drivers.
Log drowsiness events with time stamps in a database for safety monitoring and analysis.
Build a user-friendly GUI to display driver status, real-time video, and issue warnings.
Finish the course and receive a verified certificate of success.
Muhammad Yaqoob is the founder of Tentosoft Pvt Ltd and a seasoned Computer Vision expert. With 10+ years of experience and over 5,000+ students taught globally, he brings deep industry knowledge and a passion for practical, hands-on learning.
8 weeks of comprehensive training with 50+ lessons and 10+ hours of content
Driver Drowsiness Detection is a real-time computer vision solution designed to identify fatigue in drivers. It aims to prevent accidents by monitoring eye closure and yawning patterns using facial landmarks.
Introduction of the Driver Drowsiness Detection System
Monitor eye closure
Use facial landmarks
AI-based fatigue alert
This project is built to detect early signs of drowsiness using video analysis and facial geometry. It integrates AI-based methods like Eye Aspect Ratio (EAR) to determine if the driver is falling asleep while driving.
Driver Drowsiness Detection Project Overview
Monitor driver behavior
Apply EAR technique
Real-time AI solution
Essential Python packages such as OpenCV, dlib, and imutils are used for facial landmark detection, video processing, and real-time monitoring. These libraries form the technical backbone of the drowsiness detection system.
Understanding Key Packages for Driver Drowsiness Detection
Use OpenCV for video input
Support full pipeline
The EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) are calculated using facial landmarks to monitor blinking and yawning. Threshold-based logic is applied to detect prolonged eye closure and mouth openings.
Calculating EAR and MAR for Driver Drowsiness Detection
Analyzing feedback and identifying patterns
Iterating designs based on user insights
Tkinter is a Python GUI library used to build interactive user interfaces. It allows users to start video detection, view alerts, and access logs through a simple window-based application integrated with the detection logic.
Building a Tkinter GUI for Real-Time Drowsiness Detection
Build GUI with Tkinter
Control system easily
Live video streaming enables real-time monitoring of driver behavior through a webcam. The system continuously analyzes frames, processes facial features, and triggers alerts if drowsiness indicators exceed predefined thresholds.
Implementing Real-Time Drowsiness Detection with Live Video Streaming
Use webcam for input
Trigger instant alerts
Is This Course Right for You?
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