DETECTION OF DRIVER SLEEPINESS AND WARNING THE DRIVER IN REALTIME USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUES

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dc.authoridumut, ilhan/0000-0002-5269-1128
dc.authoridAKI, Ozan/0000-0002-7093-8067;
dc.authorwosidumut, ilhan/A-2772-2017
dc.authorwosidAKI, Ozan/ABG-6463-2020
dc.authorwosidAKI, Ozan/V-3842-2017
dc.authorwosidUçar, Erdem/G-6929-2014
dc.contributor.authorUmut, Ilhan
dc.contributor.authorAki, Ozan
dc.contributor.authorUcar, Erdem
dc.contributor.authorOzturk, Levent
dc.date.accessioned2024-06-12T10:59:53Z
dc.date.available2024-06-12T10:59:53Z
dc.date.issued2017
dc.departmentTrakya Üniversitesien_US
dc.description.abstractThe aim of this study is to design and implement a system that detect driver sleepiness and warn driver in real-time using image processing and machine learning techniques. Viola-Jones detector was used for segmenting face and eye images from the cameraacquired driver video. Left and right eye images were combined into a single image. Thus, an image was obtained in minimum dimensions containing both eyes. Features of these images were extracted by using Gabor filters. These features were used to classifying images for open and closed eyes. Five machine learning algorithms were evaluated with four volunteer's eye image data set obtained from driving simulator. Nearest neighbor IBk algorithm has highest accuracy by 94.76% while J48 decision tree algorithm has fastest classification speed with 91.98% accuracy. J48 decision tree algorithm was recommended for real time running. PERCLOS the ratio of number of closed eyes in one minute period and CLOSDUR the duration of closed eyes were calculated. The driver is warned with the first level alarm when the PERCLOS value is 0.15 or above, and with second level alarm when it is 0.3 or above. In addition, when it is detected that the eyes remain closed for two seconds, the driver is also warned by the second level alarm regardless of the PERCLOS value. Designed and developed real-time application can able to detect driver sleepiness with 24 FPS image processing speed and 90% real time classification accuracy. Driver sleepiness were able to detect and driver was warned successfully in real time when sleepiness level of driver is achieved the defined threshold values.en_US
dc.identifier.doi10.12913/22998624/69149
dc.identifier.endpage102en_US
dc.identifier.issn2299-8624
dc.identifier.issue2en_US
dc.identifier.startpage95en_US
dc.identifier.urihttps://doi.org/10.12913/22998624/69149
dc.identifier.urihttps://hdl.handle.net/20.500.14551/20594
dc.identifier.volume11en_US
dc.identifier.wosWOS:000406451100014en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherLublin Univ Technology, Polanden_US
dc.relation.ispartofAdvances In Science And Technology-Research Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDriveren_US
dc.subjectSleepinessen_US
dc.subjectReal Timeen_US
dc.subjectImage Processingen_US
dc.subjectMachine Learningen_US
dc.subjectDrowsiness Detectionen_US
dc.subjectSystemen_US
dc.titleDETECTION OF DRIVER SLEEPINESS AND WARNING THE DRIVER IN REALTIME USING IMAGE PROCESSING AND MACHINE LEARNING TECHNIQUESen_US
dc.typeArticleen_US

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