scholarly journals Fatigue Detection Using Computer Vision

2010 ◽  
Vol 56 (4) ◽  
pp. 457-461 ◽  
Author(s):  
Mitesh Patel ◽  
Sara Lal ◽  
Diarmuid Kavanagh ◽  
Peter Rossiter
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Face Image ◽  
Blink Rate ◽  
Eye Detection ◽  
Skin Segmentation ◽  
Video Images ◽  
Long Duration ◽  
The Face ◽  
Fatigue Detection

Fatigue Detection Using Computer VisionLong duration driving is a significant cause of fatigue related accidents of cars, airplanes, trains and other means of transport. This paper presents a design of a detection system which can be used to detect fatigue in drivers. The system is based on computer vision with main focus on eye blink rate. We propose an algorithm for eye detection that is conducted through a process of extracting the face image from the video image followed by evaluating the eye region and then eventually detecting the iris of the eye using the binary image. The advantage of this system is that the algorithm works without any constraint of the background as the face is detected using a skin segmentation technique. The detection performance of this system was tested using video images which were recorded under laboratory conditions. The applicability of the system is discussed in light of fatigue detection for drivers.

scholarly journals Implementation of face and eye detection on DM6437 board using simulink model

2020 ◽  
Vol 9 (2) ◽  
pp. 785-791
Author(s):  
B. Vijayalaxmi ◽  
Kaushik Sekaran ◽  
N. Neelima ◽  
P. Chandana ◽  
Maytham N. Meqdad ◽  
...  
Keyword(s):  
Research Work ◽  
Warning Signal ◽  
Blink Rate ◽  
Driver Fatigue ◽  
Simulink Model ◽  
Detection Stage ◽  
The Face ◽  
Fatigue Estimation ◽  
Motivating Factor ◽  
Fatigue Detection

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents.  The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

Face and Eye Detection

2011 ◽  
pp. 5-44 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Face Detection ◽  
Facial Expression Recognition ◽  
Face Image ◽  
Face Tracking ◽  
Expression Recognition ◽  
Human Face ◽  
Eye Detection ◽  
Face Image Analysis ◽  
Face Images ◽  
The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

Fatigue Detection System Based on Kalman Filtering and Dynamic Template

2013 ◽  
Vol 380-384 ◽  
pp. 3921-3924
Author(s):  
Lan Shi ◽  
Yi Hong Zhang
Keyword(s):  
Detailed Analysis ◽  
Face Detection ◽  
Kalman Filtering ◽  
Detection Rate ◽  
Traffic Accidents ◽  
Detection System ◽  
Eye Detection ◽  
New Approach ◽  
Dynamic Template ◽  
Fatigue Detection

In order to reduce the incidence of traffic accidents, it did detailed analysis and further research on fatigue detection system.this system include face detection, eye detection, eye tracking. During the eye detection, it proposed a new approach based on Kalman filtering and dynamic template. And then it did experiments on the detection rate, the PERCLOSs numerical value and the speed. Experiment results show that the detection results can meet the demand of practice. It turns out that this system can meet the demand of basic practice, has an extensive application field.

scholarly journals Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers

2019 ◽  
Vol 4 (1-2) ◽  
Author(s):  
Jelica Davidović ◽  
Dalibor Pešić ◽  
Boris Antić
Keyword(s):  
Traffic Safety ◽  
Detection System ◽  
Influencing Factor ◽  
Survey Method ◽  
Commercial Vehicles ◽  
The Road ◽  
Weakest Link ◽  
The Face ◽  
Fatigue Detection ◽  
The Impact

For decades, around the world is developing a fatigue detection system to alert drivers when they reach the state of fatigue that threatens them in traffic. Most of the research on the impact of fatigue on drivers based on driving simulators mainly because it is a controlled environment, cheap and safe approach. Since the nineties of the last century, many surveys were conducted in which the survey method was applied, while examining the subjective attitudes of drivers about the impact of fatigue on traffic safety. The beginning of the 21st century is characterized by the development of a fatigue detection system based on modern technologies, and a number of experiments were conducted. However, it not yet in use tools that can be easily detected drivers fatigue, in order to respond quickly and prevent them from operating the vehicle in such condition.The aim of this paper is to demonstrate the importance and implementation of a new fatigue identification model for commercial vehicle drivers in selected transport companies. Based on the results of this research, it is possible to determine which company is the safest from the aspect of fatigue, which is least safe. Also, the analysis of the results can determine which influencing factor is “the weakest link” among the drivers in the transport company, or where to direct measures in order to improve the road safety of the company, and therefore the local community.The study included five transport companies in Serbia, three of which are engaged in the carriage of passengers, and two transport goods. The survey used the survey method, the face face model, and 265 drivers of commercial vehicles participated, 16.6% of whom were found fatigued before the start of the shift.

scholarly journals Eye Detection System Based on Image Processing for Vehicle Safety

2020 ◽  
Vol 19 (01) ◽  
pp. 11-22
Author(s):  
Almira Budiyanto ◽  
Abdul Manan ◽  
Elvira Sukma Wahyuni
Keyword(s):  
Image Processing ◽  
Light Intensity ◽  
Digital Image Processing ◽  
Traffic Accidents ◽  
Detection System ◽  
High Accuracy ◽  
Vehicle Safety ◽  
Eye Detection ◽  
Eye Blinks ◽  
The Face

The more advanced the technology and the greater the community's need to carry out activities every day, the number of vehicles on the highway is getting crowded. From year to year, the greater the level of traffic accidents caused by many factors, among the usual reasons is the loss of awareness of the driver when driving a vehicle especially drowsiness. One of the drowsiness parameters is the frequency eye blinks. Therefore, to get the drowsiness symptoms, the purpose of this research is to detect the eye blinks, which in turn reduce the level of accidents by detecting sleepy eyes based on digital image processing. The method used to detect both eyes is the Viola-Jones method. The detection of both eyes can also acquire the duration of closed eyes and the number of eye blinks. A person can be said to be sleepy by means of sleepiness parameters determined by a study. The research shows that detection of eye blinks using the Viola-Jones method has a fairly high accuracy of up to 84.72% if the face condition is upright and tilted no more than 45 degrees. Another conclusion is that eye detection and driver detection are more effective at certain light intensity values which are around 2-33 lux.

scholarly journals Measurement of Face Detection Accuracy Using Intensity Normalization Method and Homomorphic Filtering

2017 ◽  
Vol 2 (1) ◽  
pp. 107
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Recognition System ◽  
Face Image ◽  
Normalization Method ◽  
Detection Accuracy ◽  
Normalization Methods ◽  
Homomorphic Filtering ◽  
The Face ◽  
Intensity Normalization

One of the factors that affects the detection system or face recognition is lighting. Image color processing can help the face recognition system in poor lighting conditions. In this study, homomorphic filtering and intensity normalization methods used to help improve the accuracy of face image detection. The experimental results show that the non-uniform of the illumination of the face image can be uniformed using the intensity normalization method with the average value of Peak Signal to Noise Ratio (PSNR) obtained from the whole experiment is 22.05314 and the average Absolute Mean Brightness Error (AMBE) value obtained is 6.147787. The results showed that homomorphic filtering and intensity normalization methods can be used to improve the detection accuracy of a face image.

scholarly journals Artificial Intelligence Based Face Mask Detection System

2020 ◽  
Vol 5 (8) ◽  
pp. 467-470
Author(s):  
Vivek Kumar Pandey
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
Detection System ◽  
Face Mask ◽  
Recreational Activities ◽  
Human Tendency ◽  
The Face ◽  
The Individual ◽  
Day By Day ◽  
Made In

With the advent of COVID-19 pandemic, use of mask is mandatory as per WHO/ ICMR guidelines to avert spread of CORONA virus. The post lockdown period has seen increase in cases day by day as people have now stepped out of their home to resume their work and recreational activities. Wearing mask all the time has still not found an enduring place in our day to day routine practices. It is a natural human tendency to be complacent and to remove mask while talking, working or after prolong use just use to relax and breathe properly. Thus not only risking own life but also of others who might have come in contact with the person during the period when he/she was not wearing mask. Presently the inspection of people with/ without mask is being done manually and visually by sentries/ guards present at entry/ exit points. Guards/ Sentries cannot be stationed at every place to keep a check on such people who remove their mask and roam around without restraint once they have been scrutinized at the entry gate. In the proposed system, efforts have been made in inspecting people with/ without mask automatically with the help of Computer vision and Artificial Intelligence. This module detects the face of the individual, identifies whether he/she is wearing mask or not and raises an alarm if the person is detected without wearing mask.

scholarly journals A New Roadway Eventual Obstacle Detection System Based on Computer Vision

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5109
Author(s):  
Mariano Gonzalez-de-Soto ◽  
Rocio Mora ◽  
José Antonio Martín-Jiménez ◽  
Diego Gonzalez-Aguilera
Keyword(s):  
Computer Vision ◽  
Open Source Software ◽  
Detection System ◽  
Low Cost ◽  
Obstacle Detection ◽  
Road Accidents ◽  
The Past ◽  
Video Images ◽  
Infra Red ◽  
Different Types

A new roadway eventual obstacle detection system based on computer vision is described and evaluated. This system uses low-cost hardware and open-source software to detect and classify moving elements in roads using infra-red and colour video images as input data. This solution represents an important advancement to prevent road accidents due to eventual obstacles which have considerably increased in the past decades, mainly with wildlife. The experimental evaluation of the system demonstrated that the proposed solution detects and classifies correctly different types of moving obstacles on roads, working robustly under different weather and illumination conditions.

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