scholarly journals Real-Time Driver Drowsiness Detection using Computer Vision

2021 ◽  
Vol 11 (1) ◽  
pp. 109-113
Author(s):  
Mahek Jain ◽  
◽  
Bhavya Bhagerathi ◽  
Dr. Sowmyarani C N ◽  
◽  
...  
Keyword(s):  
Computer Vision ◽  
Aspect Ratio ◽  
Real Time ◽  
Threshold Value ◽  
Transportation Safety ◽  
Road Accidents ◽  
Text To Speech ◽  
Upper Lip ◽  
Lower Lip ◽  
Driver Drowsiness

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.

scholarly journals Real Time ‘Driver Drowsiness’ & Monitoring & Detection Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 280-284
Keyword(s):  
Real Time ◽  
Transportation Safety ◽  
Small Scale ◽  
Open Area ◽  
Detection Techniques ◽  
Eye Blink ◽  
Driver Drowsiness ◽  
Day By Day

World has seen many of the accidents occur due to driver’s fatigue and a small scale distraction factor while driving the vehicle. Number of accidents has been increasing day-by-day during driving due to driver drowsiness playing as an implicating factor in many accidents. Goal of this thesis is to reduce these accidents and maintenance of transportation safety. The system are design such that it will precisely scrutiny the eye blink. Dissimilarity covering the eye will differ as per eye blink. If outturn is high the eye is closed or else out-turn is low. It shows close or open area of the eye.

scholarly journals Drivers' Real-Time Drowsiness Identification Using Facial Features and Automatic Vehicle Speed Control

2021 ◽  
Vol 3 (1) ◽  
pp. 44-52
Author(s):  
Natraj N.A ◽  
Bhavani S
Keyword(s):  
Real Time ◽  
Image Recognition ◽  
Warning System ◽  
Speed Control ◽  
Facial Features ◽  
Vehicle Speed ◽  
Road Accidents ◽  
The Road ◽  
Driver Drowsiness ◽  
Road Crash

The road crash is one of the significant problems that is of great concern in today's world. Road accidents are often caused by drivers' carelessness and negligence. The drowsy condition of the drivers, which occurs due to overwork, fatigue, and many other factors, is one of those causes. It is therefore most critical to establish systems that can detect the driver's drowsy state and provide the drivers with the appropriate warning system. In addition to the automatic speed control of the car, this system thus supports drivers in incidents by providing warnings in advance. This means that road collisions that are harmful to living lives are minimised. This is achieved by using the technique of image recognition, where driver drowsiness is observed, and using this method, simultaneous warning and speed monitoring of the vehicle is carried out.

scholarly journals PENILAIAN KELAYAKAN OBJEK PUPIL DARI FRAME CITRA MATA PADA APLIKASI PEMERIKSA MYOPIA MENGGUNAKAN STANDAR DEVIASI

2020 ◽  
Vol 3 (2) ◽  
pp. 201
Author(s):  
Teady Matius Surya Mulyana ◽  
Herlina Herlina
Keyword(s):  
Computer Vision ◽  
Standard Deviation ◽  
Real Time ◽  
Size Range ◽  
Threshold Value ◽  
Small Samples ◽  
Time Interval ◽  
Vision Assessment ◽  
Standard Deviations ◽  
Confidence Value

The process of determining vision in patients with myopia based on computer vision requires image frames containing pupil objects that are deemed to meet the requirements before entering the vision determination stage. Image frame requirements that are considered to meet the requirements are image frames that contain objects that are not too small or too large. The extraction of eye image frames in real-time results in eye image frames being captured by the camera when the eyes are blinking, so that they contain too small pupil objects. The process of determining the threshold value in binaryization of an eye image that is too large also results in pupillary objects blending with the shadows around the eye, resulting in objects that are too large. Small samples in eye image taking require treatment for statistical assessment with small samples. The small number of samples can be used to determine the feasibility of the image to enter the next process can be implemented using standard deviations. Standard deviation values can accommodate the need to limit the size range of pupil objects in the eye image that is considered feasible. The final result of this study is the implementation of the method of determining an image considered to have a pupil object of a size that is suitable for observation so that the interval is obtained according to the conditions of each set of eye images recorded by the camera during eye observation. The use of standard deviations in determining which images are considered feasible contributes to increasing the percentage of accuracy of eye vision assessment in the application of myopia vision determination in real time from 54% without standard deviation to 73% in the confidence value for an average one-time interval of 99% in the process of determining myopia vision . AbstrakProses penentuan visus pada penderita myopia berbasis  computer vision memerlukan frame-frame citra yang berisi objek pupil yang dianggap memenuhi persyaratan sebelum masuk ke tahap penentuan visus. Persyaratan frame-frame citra yang dianggap memenuhi persyaratan adalah frame-frame citra yang berisi objek dengan ukuran yang tidak terlalu kecil ataupun terlalu besar. Pengambalian frame-frame citra mata secara real-time mengakibatkan adanya frame citra mata yang ditangkap kamera ketika mata sedang berkedip, sehingga berisi objek pupil yang terlalu kecil. Proses penentuan nilai ambang pada binerisasi citra mata yang terlalu besar juga mengakibatkan objek pupil berbaur dengan bayangan di sekitar mata, sehingga menghasilkan objek yang terlalu besar. Sampel yang kecil pada pengambilan citra mata memerlukan perlakuan untuk penilaian statistik dengan sampel kecil. Jumlah sampel yang kecil dapat untuk menentukan kelayakan citra untuk masuk proses selanjutnya dapat diimplementasikan menggunakan standar deviasi. Nilai standar deviasi dapat mengakomodasi keperluan membatasi rentang ukuran objek pupil pada citra mata yang dianggap layak. Hasil akhir dari penelitian ini adalah implementasi metode penentuan suatu citra dianggap memiliki objek pupil dengan ukuran yang layak untuk diobservasi sehingga didapatkan interval yang sesuai kondisi masing-masing set citra mata yang direkam oleh kamera pada saat observasi mata. Penggunaan standar deviasi pada penentuan citra yang dianggap layak berkontribusi menaikkan persentase ketepatan penilaian visus mata pada aplikasi penentuan visus myopia secara real time dari 54% tanpa standar deviasi menjadi 73% pada nilai confidence untuk interval satu rata-rata sebesar 99% pada proses penentu visus myopia.

scholarly journals Real Time Driver Drowsiness Detection App using ML

2021 ◽  
pp. 526-530
Author(s):  
Vaibhav Mandavkar ◽  
Prasad Ligade ◽  
Shreyas Kulkarni ◽  
Chaitanya Mane ◽  
Prof. Varsha Rasal
Keyword(s):  
Machine Learning ◽  
High Speed ◽  
Threshold Value ◽  
Drinking And Driving ◽  
Road Accidents ◽  
Driver Drowsiness ◽  
Full Control ◽  
The Face ◽  
Main Factors ◽  
User Friendly

Every year road accidents are increasing rapidly as technological and mechanical advancements in vehicles permits drivers to drive at high speed. Approximately 1.35 million people die each year as a result of road accidents in India alone 151 thousand casualties were recorded last year. From this nearly 78% road accidents are caused due to driver's fault. Main factors for this accidents are drowsiness, drunk and drive and over speeding from which nearly 40% of the accidents caused due to drowsiness. People are conscious about the risk of drinking and driving but don’t realize the dangerous of drowsiness because no instruments exist to measure the driver drowsiness. If the Driver fails to concentrate on driving it reduces the driver reaction time and impairs steering behavior To solve this problem we are going to use the power of machine learning to identify if the driver is drowsy or not. Generally when someone feels drowsy his\hers eye blinking speed decreases by specifying threshold value we can detect if the driver is drowsy or not. This programme provides a mechanism for scanning facial landmarks and then using the essential landmarks for eye tracking after recognising the face. this insures that the driver is in full control of his vehicle. The system make use of device’s front camera to monitor drivers’s face to detect drowsiness and alarms the driver if system founds him drowsy. More functionality will be added to the system, such as issuing an SOS if something occurs to a car in a remote location. The software also has emergency numbers, so that in the event of an emergency, the driver may call the appropriate authorities as needed. This system may also be used for navigating by utilising the app's map functionality. Flutter will be utilised to provide a native and user-friendly system interface. As a result, the software will be available for both Android and iOS smartphones. The use of as little hardware as possible will ensure seamless processing. This technology may be employed in a variety of circumstances, including cab services, state transportation and on-road goods delivery.

scholarly journals Real-Time Driver Drowsiness Detection and Alert System

2021 ◽  
Vol 9 (VII) ◽  
pp. 3118-3126
Author(s):  
Jitha Jose
Keyword(s):  
Threshold Value ◽  
Driving Ability ◽  
Physiological Measures ◽  
Road Accidents ◽  
Drowsy Driving ◽  
Road Crashes ◽  
Driver Drowsiness ◽  
Physical Injuries ◽  
A Value ◽  
Opening And Closing

Nowadays, road accidents have become really high and is causing severe physical injuries, deaths etc, mainly in India. India has become the highest in the world in case of road accidents, recording 53 road crashes per hour. Drowsy driving is one of the reasons for road accidents. Increasing road accidents due to drowsy driving indicate the need of system that detect the drowsiness of the driver and alert them at the correct time. “Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures”[13]. Rate of using physiological measures to detect drowsiness is high; vehicle-based measures are affected by construction of the street, driving ability of the driver, type of vehicle etc; some methodologies use mental measures for which some anodes are put on the head and body. So here we have used much more feasible method. In this paper we discuss a method which is non-intrusive. In our proposed system, the main data we use to detect driver drowsiness is the eye conclusion proportion, its duration etc. Eyes opening and closing ratio reflects a person's mind status and attention and therefore, it can be potentially used to indicate driver fatigue levels[14]. We keep a value as the threshold value, if the eye conclusion proportion goes below the threshold value, then we alert the driver using a buzzer and provide tips to get rid of the drowsiness through an audio message. For continuously detecting the driver’s eye we use a camera. We also use an LED light to alert the co-passengers about the drowsiness of the driver.

scholarly journals Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

2020 ◽  
Vol 6 (3) ◽  
pp. 8 ◽  
Author(s):  
Younes Ed-Doughmi ◽  
Najlae Idrissi ◽  
Youssef Hbali
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Training Session ◽  
Primary Source ◽  
Road Accidents ◽  
Research Subjects ◽  
Real Time System ◽  
Driver Drowsiness ◽  
On The Road

In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver’s behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver’s face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents.

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh
Keyword(s):  
Processing System ◽  
Warning System ◽  
Raspberry Pi ◽  
Data Set ◽  
Lower Lip ◽  
Driver Drowsiness ◽  
Image Sensing ◽  
The Face ◽  
The Common ◽  
University Laboratory

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”

Driver’s Drowsiness Detection Using Dlib and IOT

2020 ◽  
Vol 10 ◽  
Author(s):  
Prasanna Lakshmi Kompalli ◽  
Padma Vallakati ◽  
Ganapathi Raju Nadimpalli ◽  
Vinod Mahesh Jain ◽  
Samuel Annepogu
Keyword(s):  
Aspect Ratio ◽  
The Other ◽  
Human Intervention ◽  
Road Accidents ◽  
Online Content ◽  
Facial Landmarks ◽  
Drowsiness Detection ◽  
Rear Part ◽  
Cause Of Deaths ◽  
Published Paper

Background: Road accidents are major cause of deaths worldwide. This is enormously due to fatigue, drowsiness and microsleep of the drivers. This don’t just risk the life of driver and copassengers but also a great threat to the vehicles and humans moving around that vehicle. Methods: Research, online content and previously published paper related to drowsiness are reviewed. Using the facial landmarks DAT file, the prototype will locate and get the eye coordinates and it will calculate Eye Aspect Ratio (EAR). The EAR indicates whether the driver is drowsy or not based on the result various sensors gets activated such as Alarm generator, LED Indicators, LCD message scroll, message sent to owner and engine gets locked. Results: The prototype is able to locate eyes in the frame and detect whether the person is sleepy or not. Whenever the person is feeling drowsy alarm gets generated in the cabinet on further if the person is feeling drowsy, LED indicators will start glowing, messaging will be scrolling at the rear part of vehicle so that other vehicles and humans gets cautioned and vehicle slows down and engine gets locked. Conclusion: This prototype will help in reduction of road accidents due to human intervention. It is not only helpful to the person who install it in their vehicles but also for the other vehicles and humans moving around it.

scholarly journals Conservative treatment of a Class I malocclusion with 12 mm overjet, overbite and severe mandibular crowding

2012 ◽  
Vol 17 (5) ◽  
pp. 43-52
Author(s):  
Marcos Alan Vieira Bittencourt ◽  
Arthur Costa Rodrigues Farias ◽  
Marcelo de Castellucci e Barbosa
Keyword(s):  
Class I ◽  
Facial Features ◽  
Conservative Approach ◽  
Upper Lip ◽  
Facial Aesthetics ◽  
Lower Lip ◽  
Angle Class ◽  
Nasolabial Angle ◽  
Lower Arch ◽  
Lower Incisors

INTRODUCTION: A female patient aged 12 years and 2 months had molars and canines in Class II relationship, severe overjet (12 mm), deep overbite (100%), excessive retroclination and extrusion of the lower incisors, upper incisor proclination, with mild midline diastema. Both dental arches appeared constricted and a lower arch discrepancy of less than -6.5 mm. Facially, she had a significant upper incisors display at rest, interposition and eversion of the lower lip, acute nasolabial angle and convex profile. OBJECTIVE: To report a clinical case consisting of Angle Class I malocclusion with deep overbite and overjet in addition to severe crowding treated with a conservative approach. METHODS: Treatment consisted of slight retraction of the upper incisors and intrusion and protrusion of the lower incisors until all crowding was eliminated. RESULTS: Adequate overbite and overjet were achieved while maintaining the Angle Class I canine and molar relationships and coincident midlines. The facial features were improved, with the emergence of a slightly convex profile and lip competence, achieved through a slight retraction of the upper lip and protrusion of the lower lip, while improving the nasolabial and mentolabial sulcus. CONCLUSIONS: This conservative approach with no extractions proved effective and resulted in a significant improvement of the occlusal relationship as well as in the patient's dental and facial aesthetics.

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