scholarly journals A Low-Cost Prototype for Driver Fatigue Detection

2019 ◽  
Vol 3 (1) ◽  
pp. 5
Author(s):  
Tiago Meireles ◽  
Fábio Dantas
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Infrared Camera ◽  
Physiological Parameters ◽  
Physical Parameters ◽  
Total System ◽  
Driver Fatigue ◽  
Privacy Concerns ◽  
Detection Systems ◽  
Fatigue Detection

Driver fatigue and inattention accounts for up to 20% of all traffic accidents, therefore any system that can warn the driver whenever fatigue occurs proves to be useful. Several systems have been devised to detect driver fatigue symptoms, such as measuring physiological parameters, which can be uncomfortable, or using a video or infrared camera pointed at the driver’s face, which in some cases, may cause privacy concerns for the driver. Usually these systems are expensive, therefore a brief discussion on low-cost fatigue detection systems is presented, followed by a proposal for a non-intrusive low-cost prototype, that aims to detect driver fatigue symptoms. The prototype consists of several sensors that monitor driver physical parameters and vehicle behaviour, with a total system price close to 30 euros. The prototype is discussed and compared with similar systems, pointing out its strengths and weaknesses.

scholarly journals Convolutional Two-Stream Network Using Multi-Facial Feature Fusion for Driver Fatigue Detection

2019 ◽  
Vol 11 (5) ◽  
pp. 115 ◽  
Author(s):  
Weihuang Liu ◽  
Jinhao Qian ◽  
Zengwei Yao ◽  
Xintao Jiao ◽  
Jiahui Pan
Keyword(s):  
Traffic Accidents ◽  
Road Traffic ◽  
Feature Fusion ◽  
Facial Feature ◽  
Detection Algorithm ◽  
Facial Features ◽  
Dynamic Features ◽  
Driver Fatigue ◽  
Stream Network ◽  
Fatigue Detection

Road traffic accidents caused by fatigue driving are common causes of human casualties. In this paper, we present a driver fatigue detection algorithm using two-stream network models with multi-facial features. The algorithm consists of four parts: (1) Positioning mouth and eye with multi-task cascaded convolutional neural networks (MTCNNs). (2) Extracting the static features from a partial facial image. (3) Extracting the dynamic features from a partial facial optical flow. (4) Combining both static and dynamic features using a two-stream neural network to make the classification. The main contribution of this paper is the combination of a two-stream network and multi-facial features for driver fatigue detection. Two-stream networks can combine static and dynamic image information, while partial facial images as network inputs can focus on fatigue-related information, which brings better performance. Moreover, we applied gamma correction to enhance image contrast, which can help our method achieve better results, noted by an increased accuracy of 2% in night environments. Finally, an accuracy of 97.06% was achieved on the National Tsing Hua University Driver Drowsiness Detection (NTHU-DDD) dataset.

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 Driver’s Fatigue Detection Based on Yawning Extraction

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Nawal Alioua ◽  
Aouatif Amine ◽  
Mohammed Rziza
Keyword(s):  
Traffic Accidents ◽  
Low Cost ◽  
Training Data ◽  
Video Sequences ◽  
Support Vector ◽  
New Approach ◽  
Lighting Conditions ◽  
Web Camera ◽  
Real Video ◽  
Fatigue Detection

The increasing number of traffic accidents is principally caused by fatigue. In fact, the fatigue presents a real danger on road since it reduces driver capacity to react and analyze information. In this paper we propose an efficient and nonintrusive system for monitoring driver fatigue using yawning extraction. The proposed scheme uses face extraction based support vector machine (SVM) and a new approach for mouth detection, based on circular Hough transform (CHT), applied on mouth extracted regions. Our system does not require any training data at any step or special cameras. Some experimental results showing system performance are reported. These experiments are applied over real video sequences acquired by low cost web camera and recorded in various lighting conditions.

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.

MULTISCALE DYNAMIC FEATURES BASED DRIVER FATIGUE DETECTION

2009 ◽  
Vol 23 (03) ◽  
pp. 575-589 ◽  
Author(s):  
BAO-CAI YIN ◽  
XIAO FAN ◽  
YAN-FENG SUN
Keyword(s):  
Traffic Accidents ◽  
Learning Algorithm ◽  
Real Life ◽  
Local Binary Patterns ◽  
Image Sequences ◽  
Dynamic Features ◽  
Driver Fatigue ◽  
Novel Approach ◽  
Temporal Aspect ◽  
Fatigue Detection

Driver fatigue is a significant factor in many traffic accidents. We propose a novel approach for driver fatigue detection from facial image sequences, which is based on multiscale dynamic features. First, Gabor filters are used to get a multiscale representation for image sequences. Then Local Binary Patterns are extracted from each multiscale image. To account for the temporal aspect of human fatigue, the LBP image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features from the multiscale dynamic features and construct a strong classifier for fatigue detection. The proposed approach is validated under real-life fatigue conditions. The test data includes 600 image sequences with illumination and pose variations from 30 people's videos. Experimental results show the validity of the proposed approach, and a correct rate of 98.33% is achieved which is much better than the baselines.

scholarly journals A Comparative Study of Driver Fatigue Detection Systems

2020 ◽  
Vol 6 (12) ◽  
pp. 134-140
Author(s):  
Sagrika Chandra, Manav Gupta Dr. Bhoomi Gupta and Vandana Choudhary
Keyword(s):  
Safety Concern ◽  
Road Accidents ◽  
Driver Fatigue ◽  
Long Distance ◽  
Safe Driving ◽  
Detection Systems ◽  
Driver Drowsiness ◽  
To Come ◽  
Fatigue Detection

Driver fatigue also known as driver drowsiness is caused by various factors, which can impair and hinder safe driving, including biological, physical, psychological, and other various factors. Accidents through driver fatigue are observed especially in the case where journeys are long-distance, and sleep-deprivation also plays the role of a prominent determining parameter. There is an extensive number of accidents caused by driver fatigue and these are of major safety concern; hence it is of critical importance to come up with some mechanism that can combat and prevent such road accidents to save precious lives. Thanks to technological advancements we can deploy systems that can detect driver fatigue and drowsiness. In this paper, we would be reviewing the various techniques that can be used to implement a system for the detection of driver fatigue.

scholarly journals Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 56
Author(s):  
Qaisar Abbas ◽  
Abdullah Alsheddy
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Driver Fatigue ◽  
Safe Driving ◽  
Detection Systems ◽  
Multimodal Features ◽  
Fatigue Detection

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

scholarly journals Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Jianfeng Hu
Keyword(s):  
Traffic Accidents ◽  
Single Channel ◽  
State Of The Art ◽  
Approximate Entropy ◽  
Driver Fatigue ◽  
Major Significance ◽  
Entropy Measures ◽  
Fatigue Detection ◽  
The Impact ◽  
Electroencephalogram Eeg

Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.

Driver Fatigue Detection Systems: A Review

2019 ◽  
Vol 20 (6) ◽  
pp. 2339-2352 ◽  
Author(s):  
Gulbadan Sikander ◽  
Shahzad Anwar
Keyword(s):  
Driver Fatigue ◽  
Detection Systems ◽  
Fatigue Detection

Control Cloud Data Access Privilge Anonymity with Attributed Based Encryption

2017 ◽  
Vol 7 (8) ◽  
pp. 279
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha
Keyword(s):  
Data Privacy ◽  
Low Cost ◽  
Data Access ◽  
Privacy Concerns ◽  
Cloud Data ◽  
Computing Paradigm ◽  
Attribute Based Encryption ◽  
Data Content ◽  
Identity Privacy ◽  
Cloud Servers

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.

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