Artificial Intelligence : Drowsiness Detection 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.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 DlibTask 5    About Dlib Face DetectorTask 6    About Dlib Face Region PredictorTask 7  :  Importing the Libraries.Task 8  :  Loading the dlib face regions predictorTask 9 :  Defining the Face region coordinatesTask 10 :  Using Euclidean distance to calculate the Eye Aspect RatioTask 11 :  Loading the face detector and face landmark predictorTask 12 :  Using the face region coordinates to extract the left and right eye detailsTask 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 ext