Real Time Driver Drowsiness DetectionSystem

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.

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.

With the growth in population, the occurrence of automobile accidents has also seen an increase. A detailed analysis shows that, around half million accidents occur in a year , in India alone. Further , around 60% of these accidents are caused due to driver fatigue. Driver fatigue affects the driving ability in the following 3 areas, a) It impairs coordination, b) It causes longer reaction times, and, c)It impairs judgment. Through this paper, we provide a real time monitoring system using image processing, face/eye detection techniques. Further, to ensure real-time computation, Haarcascade samples are used to differentiate between an eye blink and drowsy/fatigue detection.

Implementation of Computer Vision to Detect Driver Fatigue or Drowsiness to Reduce the Chances of Vehicle Accident, 2014

Fatigue or drowsiness is one of the main reasons of inattention of drivers. Now a day Computer Vision is a very active field to detect specific human physiology and behavior during driving to detect driver’s drowsiness. This paper proposes a design of detection system which can detect fatigue in drivers based on computer vision. The system approaches to detect fatigued drivers by first, detecting the face from a video sequence of driver’s frontal face followed by locating the eye from the extracted face. Secondly, system finds the existence of eye pupil from detected eye and measures the blink rate simultaneously and by analyzing these parameters, system measures the loss of awareness before driver completely loss his/her attention. The proposed system aims to be more cost effective than industrial installments and therefore all parameters have been analyzed under laboratory environment and frames have been captured with a low cost webcam.

Every year, road accidents caused by human error result in an increasing number of deaths and injuries around the world. Drowsy driving has been identified as a significant cause to vehicle accidents. It was demonstrated that driving performance deteriorates with increased drowsiness with resulting crashes constituting more than 20% of all vehicle accidents. Risky driving behaviours can lead to accidents, which can result in significant financial and moral damages. This paper indicates the driver's drowsiness and prevents them from the fatal accidents. Basically sleeping can be identified from several factors like eye blinking level, yawning, gripping force on the steering, brainwave (EEG) or heartrate (ECG) and so on. This work provides the WEB camera for eye blink and mouth yawn monitoring system and it also gives buzzer to alert the driver during drowsiness and stop the vehicle by applying the brake and clutch with the help of a relay. The system is based on processing the driver's eyes using Raspberry pi 4 by using a WEB camera that is affixed at the dashboard of the vehicle. This system is capable of detecting facial landmarks, we have utilized the face detection process to detect the iris point and mouth to find the threshold by computing the Eye Aspect Ratio (EAR), Eye Closure Ratio (ECR), Mouth Aspect Ratio (MAR) and Mouth Closure Ratio (MCR) to detect driver's drowsiness based on adaptive thresholding. In case the observed threshold is met then the brakes of the vehicle are applied and the driver is woken up.

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...

One of the primary causes of traffic accidents is the loss of alertness of the drivers involved. In several cases it has been found to be due to a driver falling asleep on the wheel or becoming too drowsy. Accidents caused due to driver drowsiness are difficult to prevent by traffic laws. Often, the drivers are not mindful of their lowered state of senses and the dangerous repercussions it may have and they continue driving. In some expensive vehicles, high tech cameras have been installed that seek to monitor the alertness levels of the drivers but they are not cost effective and cannot reach out to the masses. In this paper, we have outlined the design of a very simple and economical system which deals with this issue. In this system, the eye status of the driver is monitored, recorded and analyzed. The difference in the blinking pattern of an alert-driver and a drowsy-driver helps us to determine the level of alertness of the driver. If the alertness level falls below a certain threshold, an alarm is raised to warn the driver and a red beacon, attached to the rear of the vehicle, is flashed to warn other drivers. The system has been successfully tested and found to be extremely effective.

Journal of Pharmaceutical Negative Results ¦ Volume 13 ¦ Special Issue 10 ¦ 2022 , 2022

The majority of human deaths and injuries are caused by traffic accidents. A million people worldwide die each year due to traffic accident injuries, consistent with the World Health Organization. Drivers who do not receive enough sleep, rest, or who feel weary may fall asleep behind the wheel, endangering both themselves and other road users. The research on road accidents specified that major road accidents occur due to drowsiness while driving. These days, it is observed that tired driving is the main reason to occur drowsiness. Now, drowsiness becomes the main principle for to increase in the number of road accidents. This becomes a major issue in a world which is very important to resolve as soon as possible. The predominant goal of all devices is to improve the performance to detect drowsiness in real time. Many devices were developed to detect drowsiness, which depend on different artificial intelligence algorithms. So, our research is also related to driver drowsiness detection which can identify the drowsiness of a driver by identifying the face and then followed by eye tracking. The extracted eye image is matched with the dataset by the system. With the help of the dataset, the system detected that if eyes were close for a certain range, it could ring an alarm to alert the driver and if the eyes were open after the alert, then it could continue tracking. If the eyes were open then the score that we set decreased and if the eyes were closed then the score increased. This paper focus to resolve the problem of drowsiness detection with an accuracy of 80% and helps to reduce road accidents.

In the present study, a vehicle driver drowsiness warning system using image processing technique with monitoring the eye logic inference is developed and investigated. The principle of the proposed system is based on facial images analysis for warning the drowsy driver or inattention to prevent traffic accidents. The system uses a small monochrome security camera that points directly towards the driver"s face and monitors the driver"s eyes in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to alert the driver. This report describes how to find the eyes, and also how to determine if the eyes are open or closed. (Monitoring the eye logic algorithm is proposed to determine the level of fatigue by determining the state of the eye whether opened or closed accordingly.) The detail of image processing technique and the characteristic also is present in this paper. The experimental results indicated that the proposed expert system is effective for increasing safe in drive.