scholarly journals Development of Driver Fatigue Detection System By Using Video Images

2019 ◽  
pp. 26-29
Author(s):  
Burcu Kir Savas ◽  
Yasar Becerikli
Keyword(s):  
Principle Component Analysis ◽  
Traffic Accidents ◽  
Detection System ◽  
Adaboost Algorithm ◽  
Principle Component ◽  
Detection And Tracking ◽  
Video Images ◽  
The World ◽  
Eye Closure ◽  
Fatigue Detection

Major reasons for traffic accidents all over the world are mostly because of drivers' fatigue and lack of concentration. In this study, the detection and tracking of the drivers' faces in video based images were realized by using AdaBoost algorithm. The eye area was detected by using Principle Component Analysis (PCA). A predictive system was developed analyzing the eye closure of the drivers'. The system used PERCLOS (Percentage of eye closure) and it was tested on UCLA database.

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.

Implementation of Driver Fatigue Detection System with PCNN Algorithm Based on FPGA

2013 ◽  
Vol 333-335 ◽  
pp. 1060-1064 ◽  
Author(s):  
Yang Lu ◽  
Chao Gao
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Detection System ◽  
Good Stability ◽  
The Real ◽  
Design And Implementation ◽  
Car Accidents ◽  
Stability And Accuracy ◽  
Eye Closure ◽  
Fatigue Detection

This work presents the design and implementation of drivers fatigue detection system based on FPGA to prevent car accidents. According to the bright pupil phenomenon, which is produced by the retina when the incident lights wavelength is 850 nm, drivers eyes can be detected easily. While acquiring the real-time video of the drivers face by camera, the system accomplishes the detection of drivers eyes by using a simplified PCNN (pulse coupled neural network) and the computation of the PERCLOS (Percentage of Eye Closure) to decide whether the driver is fatigue or not. All the designing and accomplishments of the system are based on the FPGA platform Xilinx Virtex Pro Development Board. During the experiments, the system has the ability of processing 25 frames/sec, which is the speed of collection of the used camera. Also, the fatigue detection system has good stability and accuracy.

Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

scholarly journals A System of Driving Fatigue Detection Based on Machine Vision and Its Application on Smart Device

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wanzeng Kong ◽  
Lingxiao Zhou ◽  
Yizhi Wang ◽  
Jianhai Zhang ◽  
Jianhui Liu ◽  
...  
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Traffic Accidents ◽  
Candidate Region ◽  
Front Face ◽  
Smart Device ◽  
Adaboost Algorithm ◽  
Video Frames ◽  
Driving Fatigue ◽  
Fatigue Detection

Driving fatigue is one of the most important factors in traffic accidents. In this paper, we proposed an improved strategy and practical system to detect driving fatigue based on machine vision and Adaboost algorithm. Kinds of face and eye classifiers are well trained by Adaboost algorithm in advance. The proposed strategy firstly detects face efficiently by classifiers of front face and deflected face. Then, candidate region of eye is determined according to geometric distribution of facial organs. Finally, trained classifiers of open eyes and closed eyes are used to detect eyes in the candidate region quickly and accurately. The indexes which consist of PERCLOS and duration of closed-state are extracted in video frames real time. Moreover, the system is transplanted into smart device, that is, smartphone or tablet, due to its own camera and powerful calculation performance. Practical tests demonstrated that the proposed system can detect driver fatigue with real time and high accuracy. As the system has been planted into portable smart device, it could be widely used for driving fatigue detection in daily life.

Real-Time Driver Fatigue Detection Based on Eye State Recognition

2013 ◽  
Vol 457-458 ◽  
pp. 944-952 ◽  
Author(s):  
Chao Sun ◽  
Jian Hua Li ◽  
Yang Song ◽  
Lai Jin
Keyword(s):  
Real Time ◽  
Traffic Accidents ◽  
Region Of Interest ◽  
Principal Component ◽  
Radial Symmetry ◽  
Driver Fatigue ◽  
Adaboost Algorithm ◽  
Face Region ◽  
The Face ◽  
Eye Closure

One of the important causes of traffic accidents is driver fatigue. In this paper, a new real-time non-intrusive method to detect driver fatigue is proposed. Firstly, face region is detected by AdaBoost algorithm because of its robustness. Then a region of interest of the eye is defined based on face geometry. In this region, eye pupil is precisely located by radial symmetry transform. With principal component analysis (PCA), three eigen spaces are trained to recognize eye states. Open, closed eye samples and other non-eye samples in the face region are used to get these eigen spaces. At last, PERCLOS and consecutive eye closure time are adopted to detect driver fatigue. Experiments with thirty two participants in realistic driving condition show the reliability and the robustness of our system.

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 Survey on Driver Drowsiness Detection System

2021 ◽  
Vol 9 (12) ◽  
pp. 1850-1854
Author(s):  
Ms. K. G. Walke
Keyword(s):  
Artificial Intelligence ◽  
Road Safety ◽  
Traffic Accidents ◽  
Detection System ◽  
Optical Information ◽  
Pulse Detection ◽  
Driver Drowsiness ◽  
Heavy Trucks ◽  
Driver Sleepiness ◽  
Eye Closure

Abstract: We proposed to use this system to minimise the frequency of accidents caused by driver exhaustion, hence improving road safety. This device uses optical information and artificial intelligence to identify driver sleepiness automatically. We use Softmax to find, monitor, and analyse the driver's face and eyes in order to calculate PERCLOS (% of eye closure). It will also employ alcohol pulse detection to determine whether or not the person is normal. Due to extended driving durations and boredom in crowded settings, driver weariness is one of the leading causes of traffic accidents, particularly for drivers of big vehicles (such as buses and heavy trucks). Keywords: Driver Drowsiness, OpenCV, TensorFlow, Image Processing, Computer Vision

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