Computer Vision Image Processing

Drowsiness Detection with OpenCV

drowsiness detection

Drowsiness Detection with OpenCV

Drowsiness detection is a common problem nowadays because countless accidents have occurred as a result of driver exhaustion, tedious road conditions, and adverse weather conditions. According to the National Highway Traffic Safety Administration and the World Health Organization (WHO), over 1.35 million people die each year as a result of car accidents around the world. In most cases, road accidents are caused by insufficient driving skills. If the motorist is inebriated or drowsy, certain issues can emerge. The most common sorts of fatal accidents have been linked to the driver’s exhaustion. When drivers fall asleep behind the wheel, they lose control of the vehicle.

It is necessary to use advanced technologies to develop a smart or intelligent vehicle system. This¬†project¬†creates a technique that warns the motorist whether he or she is drowsy or daydreaming. In the behavioral-based approach, a camera analyses the driver’s eye blinking, eye closure, face detection, head posture, and so on.

Driver Drowsiness Detection System

We will use OpenCV in this Python project to collect photos from a camera and feed them into a Deep Learning model that will classify whether the person’s eyes are open or closed. For this Python project, we’ll take the following approach:

  1. Take a picture from a camera as input.
  2. Create a Region of Interest around the face in the image.
  3. Detect and feed the eyes from the ROI to the classifier.
  4. The classifier will determine whether or not the eyes are open.
  5. Calculate a score to see if the person is drowsy.

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