Artificial Intelligence : Drowsiness Detection using DLib

A practical hands on AI Project on building a Drowsiness Detection System using DLib.

If you want to learn the process to detect drowsiness while a person is driving a car with the help of AI then this course is for you.

What you’ll learn

  • What is Dlib.
  • About Dlib Face Detector.
  • About Dlib Face Region Predictor.
  • Using Euclidean distance to calculate the Eye Aspect Ratio.
  • Detecting drowsiness in live video stream in Google Colab.

Course Content

  • Introduction and Getting Started –> 3 lectures • 5min.
  • About DLIB –> 3 lectures • 4min.
  • Building a Drowsiness Detection System –> 8 lectures • 32min.
  • Project Files and Code –> 1 lecture • 1min.

Artificial Intelligence : Drowsiness Detection using DLib

Requirements

  • Basics knowledge of Python.

If you want to learn the process to detect drowsiness while a person is driving a car with the help of AI then this course is for you.

 

In this course I will cover, how to use a pre-trained DLib model to detect drowsiness. This is a hands on project where I will teach you the step by step process in building this drowsiness detector using DLib.

 

This course will walk you through the initial understanding of DLib, About Dlib Face Detector, About Dlib Face Region Predictor, then using the same to detect drowsiness of a person in a live video stream.

 

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

 

Task 1  :  Project Overview.

Task 2  :  Introduction to Google Colab.

Task 3  :  Understanding the project folder structure.

Task 4  :  What is Dlib

Task 5    About Dlib Face Detector

Task 6    About Dlib Face Region Predictor

Task 7  :  Importing the Libraries.

Task 8  :  Loading the dlib face regions predictor

Task 9 :  Defining the Face region coordinates

Task 10 :  Using Euclidean distance to calculate the Eye Aspect Ratio

Task 11 :  Loading the face detector and face landmark predictor

Task 12 :  Using the face region coordinates to extract the left and right eye details

Task 13 :  Defining a method to play the alarm.

Task 14 :  Putting it all together.

 

 

 

Almost all the statistics have identified driver drowsiness as a high priority vehicle safety issue. Drowsiness has been estimated to be involved in 10-40 per cent of crashes on motorways. Fall-asleep crashes are very serious in terms of injury severity and more likely to occur in sleep-deprived individuals.

Hence this problem statement has been picked up to see how we can solve this problem to a great extent by build a drowsiness detector.

However please note, that this has been made purely for educational purpose and refrain from using the same in real world scenarios.

In this course we are going to build a drowsiness detector and use the same to detect in live video streams.

Take the course now, and have a much stronger grasp on the subject in just a few hours!

 

 

You will receive :

 

1. Certificate of completion from AutomationGig.

2. The Jupyter notebook and other project files are provided at the end of the course in the last section.

 

 

 

So what are you waiting for?

 

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We’ll see you inside the course!

 

Happy Learning !!

 

[Music : bensound]

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