An Efficient Drowsiness Detection Scheme using Video Analysis (original) (raw)
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IJERT-Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning
International Journal of Engineering Research and Technology (IJERT), 2020
https://www.ijert.org/driver-drowsiness-monitoring-system-using-visual-behaviour-and-machine-learning https://www.ijert.org/research/driver-drowsiness-monitoring-system-using-visual-behaviour-and-machine-learning-IJERTCONV8IS12037.pdf Drowsy driving is one of the major causes of road accidents and death. Hence, detection of driver's fatigue and its indication is an active research area. Most of the conventional methods are either vehicle based, or behavioural based or physiological based. Few methods are intrusive and distract the driver, some require expensive sensors and data handling. Therefore, in this study, a low cost, real time driver's drowsiness detection system is developed with acceptable accuracy. In the developed system, a webcam records the video and driver's face is detected in each frame employing image processing techniques. Facial landmarks on the detected face are pointed and subsequently the eye aspect ratio, mouth opening ratio and nose length ratio are computed and depending on their values, drowsiness is detected based on developed adaptive thresholding. Machine learning algorithms have been implemented as well in an offline manner. A sensitivity of 95.58% and specificity of 100% has been achieved in Support Vector Machine based classification. Keywords-Drowsiness detection, visual behaviour, eye aspect ratio, mouth opening ratio, nose length ratio.
Journal of Informatics Electrical and Electronics Engineering (JIEEE), A 2 Z Journals, 2023
The process of determining if a person, generally a driver, is becoming sleepy or drowsy while performing a task such as driving is known as drowsiness detection. It is a necessary system for detecting and alerting drivers to their tiredness, which might impair their driving ability and lead to accidents. The project aims to create a reliable and efficient system capable of real-time detection of drowsiness using OpenCV, Dlib, and facial landmark detection technologies. The project's results show that the sleepiness detection method can accurately and precisely identify tiredness in real time. The technology is less intrusive and more economical than conventional sleepi-ness detection techniques. The system is based on a 68 facial landmark detector, which is a highly trained and effective detector capable of recognizing human face points. The detector aids in assessing whether the driver's eyes are closed or open. The system analyses the data collected by the detector using machine learning methods to discover patterns associated with drowsiness. When drowsiness is de-tected, the system incorporates a warning mechanism, such as an alarm or a vibra-tion in the steering wheel, to notify the driver. A variety of studies with different drivers and driving conditions were used to evaluate the performance of the real-time driver drowsiness detection system. The results show that the technology can detect tiredness properly and deliver timely warnings to the driver. This method can assist in preventing drowsy driving incidents, enhancing road safety, and saving lives. The re-sults indicated that the algorithm had an average accuracy rate of 94% for identifying tiredness in drivers.
Real Time Driver Drowsiness Detection System for Road Safety
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.
Survey Paper for Real Time Car Driver Drowsiness Detection using Machine Learning Approach
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Tiredness is a way wherein one level of mindfulness is decreased due to lacking of rest or weariness and it might cause the driver fall into rest unobtrusively. A Drowsy or lethargic driver can't decide while he/she can have a crazy rest. Nod off pulverizes are exceptionally outrageous as far as injury.Recent insights gauge that every year 1,200 passings and 76,000 wounds can be caused to fatigue or sluggishness related crashes. Over 25% of interstate car crashes are caused as consequence of driver weariness. Decrease the gamble of a mishap by advance notice the driver of his/her tiredness. This undertaking is fundamentally founded on four parts 1) Face and Eye identification: Performs scale invariant location utilizing Haar Cascade Classifier perform through a webcam. 2) Eye highlight extraction: Eye highlights are removed utilizing Hough Circle and 3) Extract single eye 4) Edge location and perform tiredness discovery on it. In the proposed technique, following the face identification step, the facial parts those are more significant and considered as the best for tiredness, are removed and followed in video succession outlines. The framework has been tried and carried out in a genuine climate. The commitment work is when tiredness recognized, after it will give alert admonition sign to the driver.
Driver Drowsiness Detection System -An Approach By Machine Learning Application
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.
Realtime Car Driver Drowsiness Detection using Machine Learning Approach
IRJET, 2022
In India around 1.5 lakh individuals kicked the bucket each year in street mishap due to sluggish. Sleepiness or Fatigue is a significant reason for street mishaps and has critical ramifications for street wellbeing. A few destructive mishaps can be forestalled in the event that the sleepy drivers are cautioned in time. Basically, Drowsiness is a condition of sluggishness which unusually happens during day time or when we are drained or when tanked. An assortment of sluggishness discovery techniques exist that screen the driver's tiredness state while driving and caution the drivers in the event that they are not focusing on driving. The pertinent elements can be separated from looks, for example, yawning, eye conclusion and set out developments toward gathering the degree of tiredness. The natural state of driver's body is broke down for driver tiredness recognition. So this application conquers the issue of tiredness recognition while driving utilizing eye extraction, facial extraction with dlib.
A REAL TIME SYSTEM FOR DETECTING DROWSINESS OF DRIVER
According to National Highway Traffic Safety Administration [NHTSA], Drowsiness/sleepiness of driver is one of the major causes of road accidents. It would, therefore, be both cost and safety beneficial if a drowsiness detection system could be developed. This paper describes a real-time non-intrusive method for detecting drowsiness of driver.
A Novel Approach for Real-Time Drowsiness Detection and Alert to Driver
Lecture Notes in Electrical Engineering
Drowsiness is the situation just before sleep. It could be due to lack of sleep, continuous long working hours, time of day and physical and mental state. It limits the ability to concentrate while driving. It leads to additional symptoms, such as forgetfulness or falling asleep at inappropriate times. Drowsiness at the wheel has been a severe problem which can be controlled using drowsiness detection system. This project is based on drowsiness detection using behavioral measures which include physical traits of human body such as facial expressions and head movement. This paper proposes an efficient and accurate system to detect driver's drowsiness by using three effective algorithms simultaneously, i.e., eye blinking, PERCLOS and head tilt techniques. A final triggering variable is obtained as resultant of contributing three algorithms. Each algorithm is set to produce the value of an intermediate variable up to a defined value, i.e., threshold value. These intermediate variables affect the value of final triggering variable to reach its threshold value. If the final variable reaches its threshold, it can be used to generate any type of alert.
Real Time Driver Drowsiness DetectionSystem
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".
Iet Communications, 2011
This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints.