Driver Drowsiness Detection System Project through Image Processing

1.  Introduction

 

This chapter includes overview of Vehicle Driver Drowsiness Detection System through Image Processing.

 

1.1  Overview:

 

Driver fatigue not only impacts the alertness and response time of the driver but it also increases the chances of being involved in car accidents. National Highway Traffic Safety Administration (NHTSA) analysis data indicates that driving while drowsy is a contributing factor to 22 to 24 percent of car crashes, and that driving while drowsy results in a four- to six times higher near-crash/crash risk relative to alert driver.

This high accident rate is due to the fact that sleepy drivers fail to take correct actions prior to a collision. An important irony in driver’s fatigue is that the driver may be too tired to realize his own level of drowsiness. This important problem is often ignored by the driver. Therefore, the use of assisting systems that monitor a driver’s level of vigilance is crucial to prevent road accidents. These systems should then alert the driver in the case of drowsiness or inattention.

 

 

1.2  Motivation:

 

Driver drowsiness is a significant factor in the increasing number of accidents on today’s roads and has been extensively accepted. This proof has been verified by many researchers that have demonstrated ties between driver drowsiness and road accidents. Although it is hard to decide the exact number of accidents due to drowsiness, it is much likely to be underestimated. The above statement shows the significance of a research with the objective of reducing the dangers of accidents anticipated to drowsiness. So far, researchers have tried to model the behavior by creating links between drowsiness and certain indications related to the vehicle and to the driver

 

2.  LITERRATURE REVIEW

 

2.1  Driver Drowsiness Monitoring Based on Yawning Detection

Shabnam Abtahi1, Behnoosh Hariri2

            The mouth geometrical features are used to detect the yawn. The system will alert the driver of his fatigue and the improper driving situation in case of yawning detection.

This paper is organized as follows: in section II the related work about the detection of driver fatigue is presented. Section III describes the method of approaching the goal of the paper. Experimental results are shown on section IV and finally section V presents the conclusion and future studies.

 

2.2   Driver Drowsiness Monitoring Based on Eye and Yawn Detection

Ms. Shubhangi Kalyane, Ms. Parmindar Kaur

In this paper, the following method is used. we record the drivers face using a camera that is installed the vehicle. In order to detect the yawn, the first step is to detect and track the face using the series of frames shots taken by the camera. We can then detect the location of the eyes and the mouth in the captured face by finding out the eye map and mouth map respectively. These geometrical features are then used to detect weather the person is drowsy or not.

 

2.3  Driver Drowsiness Detection System Using MATLAB

Ms. Shalini Kashyap1, Mr. V.K Sharma2

           In this paper, the system deals with using information obtained for the binary version of the image to find the edges of the face, which narrows the area of where the eyes may exist. Once the eyes are located, measuring the distances between the intensity changes in the eye area determine whether the eyes are open or closed. If the eyes are found closed for 5 or more consecutive frames, then the system finds the inactiveness of the driver and concludes that the driver is falling asleep and issues a warning signal or generate and alarm signal to wake him up.

 

3.  SYSTEM DESIGN

 

The driver drowsiness detection system consists of different modules to properly analyze changes in the face of driver. These modules are categorized as:

1.    Camera

2.    Image Processing

3.    Detection

4.    Alarm

3.1  Camera

The Drowsy Driver Detection system consists of a camera that takes images of the driver’s face. The camera is placed in front of the driver, approximately 30 cm away from the face. The camera must be positioned such that the following criteria are met: 1. The driver’s face takes up the majority of the image. 2. The driver’s face is approximately at the center of the image.

3.2  Image Processing

Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Nowadays, image processing is among rapidly growing technologies. It forms core research area within engineering and computer science disciplines too.

Image processing basically includes the following three steps:

·         Importing the image via image acquisition tools;

·         Analyzing and manipulating the image;

·         Output in which result can be altered image or report that is based on image analysis.

 

3.3  Detection

At this stage, we will proceed with the identification of drowsiness patterns. To process the patterns will begin with the isolation of the section of interest as the eyes. Then, the process of extracting characteristics begins, which analyzes the moment of closing the eyes and the distance from the opening of the eyes.

3.4  Alarm

Finally, after detection, the system will emit an audible alarm to warn the driver to have drowsiness. The alarm varies according to the pattern that has been detected in relation to the sensitivity level. So the driver does not get used to a repetitive tone and ignores the warning

Fig: Block Diagram

 

3.5 Data Flow Diagram

 

3.5.1    DFD1:

 

Fig.3.2.1: Data Flow Diagram

For this system, it will take the processing of images through a camera which will focus on the driver.  In that, it is going to analyze the changes that happen in the face and then will be processed through a program in order to detect drowsiness to send an alert to the driver. If Driver have drowsiness then by using alarm, we are going to inform that driver.