REAL TIME DROWSY DRIVER DETECTION USING HAARCASCADE SAMPLES

International Journal of Circuits, Systems and Signal Processing, 2021

In recent years, one of the most important factors in road accidents is the drowsiness of drivers and the distraction while driving. In this paper, we describe a system that monitors the detection of fatigue or drowsiness. The proposed solutions follow the driver's gaze, and if the system identifies the closed eyes, it triggers an alarm signal intended to alert against losing control of the car and causing traffic accidents. Eye-tracking is the process that measuring the eye position and eye movement. The proposed method is structured in three phases. In the first phase, eye images are captured at constant time intervals and converted into grayscale images. In the second phase these images are fed to Haar algorithm to identify the driver eyes. In the third phase, based on the previous phase the system can now take action to continue monitoring or trigger alarm to alert the driver if the drowsiness has been detected.

International Journal of Advanced Research in Electrical, Electronics and Instrumentation Energy, 2014

Nowadays, the main reason of road accidents is the drowsiness of driver. In this paper, we are focusing on designing of a system that will monitor the open or close state of drivers eyes in real time. Video camera is placed on a car desk in front of driver for monitoring eyes state of the driver. This system in turn detects drowsiness of driver. The system uses Viola Jones method which detects objects in the images i.e. detects face and eye localization is done by Haar like features. If eyes remain closed for the successive frames, system gives indication as "drowsy driver".

2014

Now a days the driver drowsiness is leading cause for major accidents. The regular monitoring of drivers drowsiness is one of the best solution in order to reduce the accidents caused by drowsiness. In order to detect and remove this cause of road accident many driver fatigue detection methods have been proposed.Consequently, it is very necessary to design a road accidents prevention system by detecting driver’s drowsiness, which determines the level of driver inattentiveness and give a warning when an impending danger exists. In this paper, a simulation and analysis of fusion method has done. This method of eye blinking and yawning detection is based on the changes in the mouth geometric features. The programming for this is done in OpenCV using the Haarcascade library for the detection of facial features and Active Contour Method for the activity of lips . Keywords— Driver Face Detection, Driver Eye Blink Detection, Driver Yawning Detection, Driver Drowsiness, Real time system, RO...

Academic Perspective Procedia, 2021

Drowsiness is one of the underlying causes of driving accidents leading to serious injuries and deaths annually. According to the experts has mentioned that almost 30% of all traffic accidents have been caused by drowsiness. In avoiding these traffic accidents, a proper system is required to prevent the driver from falling asleep. This study proposes a real-time image processing-based system for recognizing the drowsiness face expression of the vehicle driver. The method of this study, detecting the exact position facial landmarks and both left and right eyes using dlib and eye aspect ratio algorithm. This system, after detecting drowsiness eye, give audible alert the vehicle driver to stay awake throughout the driving journey.

International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020

The project proposes an associate degree IOT (Internet of things) that monitors the important time behaviour of the driving force, to overcome the accident occurred because of asleep driving. The face, a very important part of the body, conveys a great deal of knowledge. Once a driver is in a state of fatigue, the facial expressions, e.g The frequency of blinking and yawning, are completely different from those within the traditional state. The device that detects the drivers' fatigue standing like yawning, blinking, and period of eye closure, mistreatment video pictures. We introduce Haar Cascade face tracking algorithmic programs to enhance the chase accuracy. Further, we have a design replacement detection technique for facial regions supporting sixty eight key points. Then we have a tendency to use these facial regions to gauge the drivers' state. By combining the options of the eyes and mouth, the device will alert the driver using an alarm.

In recent years driver fatigue is one of the major causes of vehicle accidents in the world. Due to these accidents, many people lose their lives or get severely injured. A direct way of measuring driver fatigue is measuring the state of the driver i.e. drowsiness. So, it is very important to detect the drowsiness of the driver to save life and property. This work is aimed towards developing a prototype for drowsiness detection and accident prevention using Raspberry Pi. This system is a real-time system which captures image continuously and measures the state of the eye according to the specified algorithm and gives a warning if required. So, when the closure of the eye exceeds a certain amount which is observed by the camera module connected to the Raspberry Pi, the driver is identified to be sleepy and an alert is given in the form of a buzzer or any other sound from the Pygame module present in the Raspbian OS. If the alerts are found to be more than a particular count, the processor gradually reduces the speed of the vehicle and the system ignition is set to off state by stopping the motor and send the email to the registered person. If there aren't any alerts i.e. the eye closure range is within a certain threshold, then the system lets the engine run without any interruption. This system continuously monitors the drowsiness of the driver and thus helps in reducing the possibility of occurring accidents

Many Driver Drowsiness Detection systems have been developed using eye and face detection methods but in this paper we have advanced the previous systems by adding the concept of head position technique. Till now none of the systems developed, have used the head position of the driver to detect the drowsiness, every paper have focused only on the face and eye detection concept. The head position technique is a newly introduced feature which enhances the performance of the system to a great extent. The systems capture frame and detects the face and eyes using HAAR-like classifiers, if the face is detected and eyes are closed then head position is monitored for next few frames, if the face is aligning down gradually and continuously then the alarm is activated.

Driver inattention is one of the main causes of traffic accidents. Monitoring a driver to detect inattention is a complex problem that involves physiological and behavioral elements. Different approaches have been made, and among them Computer Vision has the potential of monitoring the person behind the wheel without interfering with his driving. In this paper I have developed a system that can monitor the alertness of drivers in order to prevent people from falling asleep at the wheel. The other main aim of this algorithm is to have efficient performance on low quality webcam and without the use of infrared light which is harmful for the human eye. Motor vehicle accidents cause injury and death, and this system will help to decrease the amount of crashes due to fatigued drivers. The proposed algorithm will work in three main stages. In first stage the face of the driver is detected and tracked. In the second stage the facial features are extracted for further processing. In last stage the most crucial parameter is monitored which is eye's status. In the last stage it is determined that whether the eyes are closed or open. On the basis of this result the warning is issued to the driver to take a break.

2014

The increasing number of traffic accidents due to a driver's diminished vigilance level has become a serious problem for society. Some of these accidents are the result of the driver's medical condition. However, a majority of these accidents are related to driver fatigue, drowsiness of drivers. Car accidents associated with driver fatigue are more likely to be serious, leading to serious injuries and deaths. In this paper, an algorithm for drivers' drowsiness detection based on eye states tracking is presented. I use a database which contains 150 images captured from different people in various conditions for better results. These images are captured using a camera located in a car in front of the driver. These images are used for tracking as input of the proposed method. In first step I use color space for drivers' face detection and crop the face from background. In the next step, I estimate the area of the eyes and crop image from this region. Then top and bottom...

International Journal of Scientific Research in Science and Technology, 2017