Visual distraction detection for safety driving

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dc.contributor.authorKhurram Shahbaz
dc.contributor.authorBadar Nasir
dc.contributor.authorHassam Zahid
dc.contributor.authorMuhammad Usman
dc.date.accessioned2026-01-28T13:20:10Z
dc.date.available2026-01-28T13:20:10Z
dc.date.issued2017
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 frames.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/19098
dc.language.isoen_US
dc.publisherumt lahore
dc.titleVisual distraction detection for safety driving
dc.typeThesis
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