Driver Drowsyness Detection System Using Computer Vision

Driver Drowsyness Detection System Using Computer Vision

₹999.00

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The Driver Drowsiness Detection System is a real-time computer vision project designed to monitor a driver’s facial features through a webcam and identify signs of drowsiness based on eye closure and yawning.

The system uses MediaPipe Face Landmarker to detect facial landmarks and calculates Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) to analyze eye and mouth movements.

Key Features

  • Real-time webcam-based monitoring
  • Eye closure detection using EAR
  • Yawning detection using MAR
  • Drowsiness status detection
  • Eye-closed and yawning counters
  • Facial landmark visualization
  • Face bounding box
  • Live status dashboard
  • Start/Stop live detection
  • Simple desktop GUI using Tkinter

Technologies

Python | OpenCV | MediaPipe | NumPy | Tkinter | Pillow

What You Get

  • Complete Python source code
  • Required MediaPipe model file
  • Ready-to-run project structure
  • Detection and GUI implementation

Suitable For

B.Tech/M.Tech Projects • AI/ML Projects • Computer Vision Projects • Python Projects • Academic Demonstrations

Important Note

This project is intended for educational, demonstration, learning, and research purposes.

It is a computer-vision prototype and should not be treated as a certified automotive safety system or relied upon as the sole mechanism for preventing accidents.

Performance may vary depending on camera quality, lighting conditions, camera position, driver’s face orientation, individual facial characteristics, and other environmental factors.

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