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Driver Fatigue Detection Using Genetic Algorithm

In this paper, we apply clustering algorithm to... [Show full abstract]Read moreConference PaperA multi-objective covariance matrix adaptation evolutionary strategy based on decomposition for anal...November 2013So-Youn ParkJu-Jang LeeSound localization has been utilized As the influences of these characteristics on driver fatigue are quite different from each other, we propose a genetic algorithm (GA)-based neural network (NN) system to fuse these three parameters. Tarrytown, NY, USA tableofcontents doi>10.1016/j.eswa.2016.06.042 2016 Article Bibliometrics ·Citation Count: 0 ·Downloads (cumulative): n/a ·Downloads (12 Months): n/a ·Downloads (6 Weeks): n/a Tools and Resources Save to Binder Export Formats: The best fit model with the lowest mean absolute errors (MAE) between the observed and estimated values is selected for future estimations. Homepage

To investigate the performance of the ANN model for future estimations, a fifteen year period from 2006 to 2020 with two possible scenarios was employed. The results of the GA model showed that the exponential model form was suitable to estimate the number of accidents and fatalities while the linear form was the most appropriate for For model development, the number of vehicles (N), fatalities, injuries, accidents and population (P) were selected as model parameters. The ACM Guide to Computing Literature All Tags Export Formats Save to Binder

Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.Learn moreLast Updated: 20 Feb 17 © 2008-2017 researchgate.net. Such intelligent networked environments will entirely depend on real time monitoring and real time profiling, resulting in real time adaptation of the environment. Several driver monitoring systems (fatigue/emotions) have been proposed in the past which include intrusive and nonintrusive techniques101112131415161718192021. Proceedings, Applications of Computer Vision, 6th IEEE Workshop, December 3–4, Orlando, Florida, USA, pp 137–142Google Scholar3.D'Orazio T, Leo M, Spagnolo P, et al. (2004) A neural system for eye detection in

Proceedings, Intelligent Transportation Systems, 7th International IEEE Conference, October 3–6, Washington, D.C., USA, pp 657–662Google Scholar5.Hashim MF, Saad P, Juhari MRM, et al. (2005) A face recognition system using template matching Three typical characteristics of driver fatigue are involved, pupil shape, eye blinking frequency, and yawn frequency. Full-text · Article · Apr 2009 Ali Payıdar AkgüngörErdem DoğanRead full-textThe script could, for instance, be constitutive for the competence to drive a car: violating the legal norms that constitute this doi:10.1007/s10015-006-0406-8 286 Downloads AbstractNowadays, many traffic accidents occur due to driver fatigue.

Since each type of features in turn contains several different values, given a single fifteen-frame sequence, the correlation coefficients between those features of the same type can form the attribute vector Three typical characteristics of driver fatigue are involved, pupil shape, eye blinking frequency, and yawn frequency. The experimental results show that the proposed method is robust and efficient. http://ieeexplore.ieee.org/document/4579992/ Although filtering and amplification can be implemented in digital, analog circuit is needed because of sampling aliasing problem and limitation of computational resources in real-time application.

Read our cookies policy to learn more.OKorDiscover by subject areaJoin for freeLog in An error occurred while rendering template. Additionally, the changes in a driver's performance are more complicated and not reliable. Subscribe IEEE Account Change Username/Password Update Address Purchase Details Payment Options Order History View Purchased Documents Profile Information Communications Preferences Profession and Education Technical Interests Need Help? Log InSign Upmore Job BoardAboutPressBlogPeoplePapersTermsPrivacyCopyrightWe're Hiring!Help Centerless Log InSign Up pdfDROWSINESS DETECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES8 PagesDROWSINESS DETECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUESUploaded byIaset Usconnect to downloadGetpdfDROWSINESS DETECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUESDownloadDROWSINESS DETECTION USING

Not logged in Not affiliated For full functionality of ResearchGate it is necessary to enable JavaScript. https://books.google.com/books?id=5R1EAAAAQBAJ&pg=PA1263&lpg=PA1263&dq=driver+fatigue+detection+using+genetic+algorithm&source=bl&ots=nSgDPOIJ9o&sig=spLz_puL0vo7sR8lemLnk_kme_4&hl=en&sa=X&ved=0ahUKEwiO7ZK-vKHTAhVCkRQKHd Additionally, the changes in a driver's performance are more complicated and not reliable. In this study, the infant face region is segmented based on the skin colour information. Take survey Buy options Artificial Life and RoboticsJanuary 2007, Volume 11, Issue 1, pp 87–90Driver fatigue detection using a genetic algorithmAuthorsAuthors and affiliationsShanshan JinEmail authorSo-Youn ParkJu-Jang LeeORIGINAL ARTICLEFirst Online: 25 January 2007Received: 07 July 2006Accepted:

At the same time potential customers are profiled to detect their habits and preferences in order to provide for targeted services. Bonuses The comparison of the model results indicated that the performance of the ANN model was better than that of the GA model. Publisher conditions are provided by RoMEO. A monitoring technology which detects driver fatigue (Jin, Park et al. 2007) could, in combination with a device that affects the accelerator or even the motor, prevent a driver from continuing

A web camera as a vision sensor is located to acquire video-images of the driver. US & Canada: +1 800 678 4333 Worldwide: +1 732 981 0060 Contact & Support About IEEE Xplore Contact Us Help Terms of Use Nondiscrimination Policy Sitemap Privacy & Opting Out A web camera as a vision sensor is located to acquire video-images of the driver. http://alpinedesignsmtb.com/driver-fatigue/driver-fatigue-detection-device.php Here are the instructions how to enable JavaScript in your web browser.

Because of the complexity of the problem, Multi-objective problems (MOPs) as well as global optimization problem has been developed so far, but parents for genetic reproduction has been considered as one Non-intrusive Car Driver’s Emotion Recognition Using Thermal CameraData · Jan 2016 · TransportAbhiram KolliAlireza FasihFadi Al MachotKyandoghere KyamakyaKyandoghere KyamakyaReadMuhammad A good level of research has been documented in the area of In the first scenario, the annual average growth rates of population and the number of vehicles are assumed to be 2.0 % and 7.5%, respectively.

Driver fatigue detection based on computer vision is one of the most hopeful applications of image recognition technology.

rgreq-e8ea2134be6fe61a35effb332362790f false Skip to Main Content IEEE.org IEEE Xplore Digital Library IEEE-SA IEEE Spectrum More Sites Cart(0) Create Account Personal Sign In Personal Sign In Username Password Sign In Forgot Password? We use the GA to determine the structure of the neural network system. When fatigue monitoring is implemented on a real model, it is difficult to predict the driver fatigue accurately or reliably based only on a single driver behavior. Theoretical and empirical studies of the parameters and strategies have been conducted, and numerous variants have been proposed.

In the ANN model, the sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. Opposition-based DE (ODE), one of such variants, combines DE with opposition-based learning (OBL) to obtain a high-quality... [Show full abstract]Read moreArticleSelf-Diagnosis Device Using Wrist PulseJanuary 2007So-Youn ParkJu-Jang LeeTo diagnose disease, western See all ›27 CitationsSee all ›7 ReferencesShare Facebook Twitter Google+ LinkedIn Reddit Request full-text Driver fatigue detection using a genetic algorithmArticle · January 2007 with 45 ReadsDOI: 10.1007/s10015-006-0406-8 1st Shanshan Jin2nd So-Youn Park3rd Ju-Jang LeeAbstractNowadays, find this Neural Network corresponding to each type of those features has been constructed in order to classify these facial expressions.

Legal and Technological Normativity: More (and less) than twin sisters[Show abstract] [Hide abstract] ABSTRACT: Within science technology and society studies the focus has long been on descriptive micro-analyses. Did you know your Organization can subscribe to the ACM Digital Library? Many efforts have been made to develop fatigue monitoring, but most of them focus on only a single behavior, a feature of the eyes, or a head motion, or mouth motion, We use the GA to determine the structure of the neural network system.

There are several factors that reflect driver's fatigue. Th e main objective of this study is to develop models for the future estimates of the number of accidents , fatalities and injuries in Ankara, Turkey applying ANN and GA Illumination and pose variations are considered as major concerns in facial emotion recognition222324252627. Each of the congress themes was chaired by two leading experts.

Several authors have raised the issue of the normative implications of the findings of research into socio-technical devices and infrastructures, while some claim that material artifacts have moral significance or should Boyraz and colleagues (Boyraz, Acar et al., 2008) have compared a neural network approach with a fuzzy inference system and it was concluded that there was no significant difference between the There are several factors that reflect driver's fatigue. For good performance, sound signal obtained from the microphone has to be well filtered and amplified.

Artif Life Robotics (2007) 11: 87. Both industry and the European Commission are investing huge sums of money into what they call Ambient Intelligence and the creation of an Internet of Things. What should be done to make research data more openly available? Proceeding, Intelligent Transportation Systems, 7th International IEEE Conference, October 3–6, Washington, D.C., USA, pp 320–325Google Scholar4.Zhiwei Z, Qiang J (2004) Real time and non-intrusive driver fatigue monitoring.

You can download the paper by clicking the button above.READ PAPERGET pdf ×CloseLog InLog InwithFacebookLog InwithGoogleorEmail:Password:Remember me on this computerorreset passwordEnter the email address you signed up with and we'll email In this article, we represent a model that simulates a space in a real car. All participants will receive our resulting report. The authors have used skin filter method to detect the face121314.