Visual Distraction Detection for Safety Driving

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dc.contributor.authorKhurram Shahbaz
dc.contributor.authorBadar Nasir
dc.contributor.authorZahid, Hassam
dc.contributor.authorUsman, Muhammad
dc.date.accessioned2018-02-12T11:02:00Z
dc.date.available2018-02-12T11:02:00Z
dc.date.issued2016
dc.descriptionSupervised by: Syed Farooq Alien_US
dc.description.abstractEvery day we see and hear about road accidents caused by irresponsible behavior of the drivers. The majority of the misfortunes happen because of the eye off the road while driving, not concentrating on the road signs and also of driver's distraction from the road. This project is here to discuss and highlight the driver's facial motion distraction and gives methods which use facial points and head rotation of the driver to indicate the problem. These facial points are detected by ASM and Boosted Regression with Markov Networks (BoRMaN). Classifiers like (Neural Networks (Multilayer Perceptron (MLP)), Naïve Byes, J48, Decision Table, NNGE, SMO (Support Vector Machine (SVM)) and Adaboost were used to prepare and test the features of various framesen_US
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/2664
dc.language.isoenen_US
dc.publisherUniversity of Management and Technologen_US
dc.subjectNeural Networksen_US
dc.subjectMultilayer Perceptronen_US
dc.subjectBS thesisen_US
dc.titleVisual Distraction Detection for Safety Drivingen_US
dc.titleVisual distraction detection for safety drivingen_us
dc.typeThesisen_US
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