Driver Drowsiness Detection System

The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert syste

Project Title

Project Area of Specialization

Internet of Things

Project Summary

Project Objectives

Driver drowsiness detection is a car safety technology which helps to save the life of the driver by preventing accidents when the driver is getting drowsy.
The main objective is to first design a system to detect driver’s drowsiness by continuously monitoring retina of the eye.
The system works in spite of driver wearing spectacles and in various lighting conditions.
To alert the driver on the detection of drowsiness by using buzzer or alarm.
Speed of the vehicle can be reduced.
Traffic management can be maintained by reducing the accidents.

Project Implementation Method

Python: Python is the basis of the program that we wrote. It utilizes many of the python libraries.
Laptop: Used to run our code.
Webcam: Used to get the video feed.
Libraries
- Numpy: Pre-requisite for Dlib
- Scipy: Used for calculating Euclidean distance between the eyelids.
- Playsound: Used for sounding the alarm
- Dlib: This program is used to find the frontal human face and estimate its pose using 68 face landmarks.
- Imutils: Convenient functions written for Opencv.
- Opencv: Used to get the video stream from the webcam, etc.

V OS: Program is tested on Windows 10

Step 1 – Take image as input from a camera.

Step 2 – Detect the face in the image and create a Region of Interest (ROI).

Step 3 – Detect the eyes from ROI and feed it to the classifier.

Step 4 – Classifier will categorize whether eyes are open or closed.

Step 5 – Calculate score to check whether the person is drowsy.

Benefits of the Project

A Portable System
Accuracte results in detetcting the drowsiness
High quality camera to take clear images
Prevent incidents occurs due to drowsiness
Soft alarming technique
User interaction rate is good
All time detection when driving a vehicle
Yawn No-Yawn detection

Technical Details of Final Deliverable

Drivers face is continuously monitored using a video camera. In order to detect the drowsiness the first step is to detect the face using the series of frame shots taken by the camera. Then the location of the eyes is detected and retina of the eye is
continuously monitored. The captured image is sent to the Raspberry Pi board for image processing. The raspberry Pi converts the received image to digital signal using Open CV.
The digital signal is transmitted from transmitter to the receiver. Both the transmitter and the receiver are paired up. The signal is then passed to the LPC2148, the microcontroller. If the signal crosses the threshold of two seconds, then the alarm beeps and the speed of the vehicle is automatically reduced.