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dc.contributor.authorZiryawulawo, Ali
dc.date.accessioned2021-02-26T07:34:29Z
dc.date.available2021-02-26T07:34:29Z
dc.date.issued2021
dc.identifier.citationZiryawulawo, A. (2021). A Machine learning Based Driver Monitoring System for the Kayoola EVS. (Unpublished undergraduate dissertation) Makerere University: Kampala, Ugandaen_US
dc.identifier.urihttp://hdl.handle.net/20.500.12281/9026
dc.descriptionA research report submitted in partial fulfillment of the requirements for the award of the degree of Bachelor of Science in Electrical Engineeringen_US
dc.description.abstractDriver Drowsiness and sleeping while driving are one of the causes of fatal accidents and highway crashes by drivers. Therefore in an effort to ensure safety for the Kayoola EVS passengers, Kiira Motors aims at developing efficient Advanced Driver Assistance Systems that will improve safety for both the driver and passengers. This project aims at developing a driver assistance system to monitor the driver's alertness and vigilance while driving to prevent drowsiness related crashes. A driver monitoring system comprises of different phases. This was achieved in a step by step process which included Face Detection, Face tracking, Eye Detection, Eye tracking and then eye state classi cation.en_US
dc.language.isoenen_US
dc.subjectDriver Monitoring Systemen_US
dc.subjectKayoola EVS.en_US
dc.titleA Machine learning Based Driver Monitoring System for the Kayoola EVS.en_US
dc.typeThesisen_US


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