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.

scholarly journals A novel real-time driving fatigue detection system based on wireless dry EEG

2018 ◽  
Vol 12 (4) ◽  
pp. 365-376 ◽  
Author(s):  
Hongtao Wang ◽  
Andrei Dragomir ◽  
Nida Itrat Abbasi ◽  
Junhua Li ◽  
Nitish V. Thakor ◽  
...  
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driving Fatigue ◽  
Fatigue Detection

Machine Vision Recognition of Weld Pool in Gas Tungsten Arc Welding

1995 ◽  
Vol 209 (2) ◽  
pp. 141-152 ◽  
Author(s):  
R Kovacevic ◽  
Y M Zhang
Keyword(s):  
Machine Vision ◽  
Real Time ◽  
Weld Pool ◽  
Visual Information ◽  
Vision System ◽  
Tracking Error ◽  
Seam Tracking ◽  
Front Face ◽  
Welding Parameters ◽  
Weld Penetration

The weld pool and its surrounding area can provide a human welder with sufficient visual information to control welding quality. Seam tracking error and pool geometry can be recognized by a skilled human welder and then utilized to adjust the welding parameters. However, for machine vision, accurate real-time recognition of weld pool geometry is a difficult task due to the high intensity arc light, even though seam tracking errors can be detected. A novel vision system is, therefore, used to acquire quality images against the arc. A real-time recognition algorithm is proposed to analyse the image and recognize the pool geometry based on the pattern recognition technique. Despite surface impurity and other influences, the pool geometry can always be recognized with sufficient accuracy in 150 ms under different welding conditions. To explore the potential application of machine vision in weld penetration control, experiments are conducted to show the correlation between pool geometry and weld penetration state. Thus, pool recognition also provides a possible technique for front-face sensing of the weld penetration.

scholarly journals Heterogeneous Signal Fusion Method in Driving Fatigue Detection Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qingjun Wang ◽  
Zhendong Mu
Keyword(s):  
Traffic Accidents ◽  
Information Source ◽  
Steering Wheel ◽  
Detection Accuracy ◽  
Fusion Method ◽  
Eyelid Closure ◽  
Signal Fusion ◽  
Fusion Methods ◽  
Driving Fatigue ◽  
Fatigue Detection

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver’s breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%.

scholarly journals Driving Fatigue Detection from EEG Using a Modified PCANet Method

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yuliang Ma ◽  
Bin Chen ◽  
Rihui Li ◽  
Chushan Wang ◽  
Jun Wang ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Deep Learning ◽  
Classification Accuracy ◽  
Traffic Accidents ◽  
Principal Component ◽  
Learning Model ◽  
Driving Fatigue ◽  
Fatigue Detection ◽  
Deep Learning Model

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection.

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.

A Multivariate Weighted Ordinal Pattern Transition Network for Characterizing Driver Fatigue Behavior from EEG Signals

2020 ◽  
Vol 30 (08) ◽  
pp. 2050118
Author(s):  
Yu-Xuan Yang ◽  
Zhong-Ke Gao
Keyword(s):  
Traffic Accidents ◽  
Fatigue Behavior ◽  
Multivariate Time Series ◽  
Detection Task ◽  
Eeg Signals ◽  
Driver Fatigue ◽  
Driving Fatigue ◽  
Fatigue Detection ◽  
Ordinal Pattern ◽  
Fatigue Mechanism

Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extracted from the generated MWOPT network. Furthermore, the nodal degree index is input into a classifier to distinguish the fatigue state from alert state. The obtained high accuracy indicates the effectiveness of the proposed network for EEG-based fatigue detection. Besides, four nodes are found to play an important role in identifying fatigue state. These results suggest that the proposed method enables to analyze nonlinear multivariate time series and investigate the driving fatigue behavior.

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.

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