Application of CNN based architectures in detection of distracted drivers

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Бібліографічні деталі
Автори:	Arafin, Irfana, Islam, Md Mahirul, Tazwar, Syed Ittisaf, Das, Nilay Shuvra, Anika, Sabrina Tabassum
Інші автори:	Bin Ashraf, Faisal
Формат:	Дисертація
Мова:	English
Опубліковано:	Brac University 2023
Предмети:	Deep learning Machine learning Distracted driving Prediction Decision tree Linear regression analysis Deep learning (Machine learning)
Онлайн доступ:	http://hdl.handle.net/10361/21924

id	10361-21924
record_format	dspace
spelling	10361-219242023-12-07T10:15:46Z Application of CNN based architectures in detection of distracted drivers Arafin, Irfana Islam, Md Mahirul Tazwar, Syed Ittisaf Das, Nilay Shuvra Anika, Sabrina Tabassum Bin Ashraf, Faisal Department of Computer Science and Engineering, Brac University Deep learning Machine learning Distracted driving Prediction Decision tree Linear regression analysis Deep learning (Machine learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 69-71). Distracted driving is known to be one of the most significant reasons behind the occurrence of traffic accidents. Moreover, the phenomenon of the occurrence of road accidents due to distracted driving has been increasing at a high rate in recent years. Previously, different machine learning and neural network-based approaches were taken to find out the best possible way of detecting distracted driving. This work proposes an effective interpretation which is to detect the distraction of drivers through a Deep Learning approach through the implementation of several Convo lutional Neural Network (CNN) based architectures. The results presented in this research is to confirm the better accuracy and success rate of the Deep Learning approach to detect distracted driving behaviors demonstrating the potentiality of this method to help measure unusual driving performance. The proposed custom CNN model not only ensures an impressive accuracy but also it’s ability to interpret the proper regions of interests on two datasets of distracted driving Irfana Arafin Md Mahirul Islam Syed Ittisaf Tazwar Nilay Shuvra Das Sabrina Tabassum Anika B.Sc. in Computer Science 2023-12-05T09:32:07Z 2023-12-05T09:32:07Z 2022 2022-08 Thesis ID: 22241181 ID: 18101347 ID: 18301137 ID: 22241166 ID: 19301111 http://hdl.handle.net/10361/21924 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 71 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Deep learning Machine learning Distracted driving Prediction Decision tree Linear regression analysis Deep learning (Machine learning)
spellingShingle	Deep learning Machine learning Distracted driving Prediction Decision tree Linear regression analysis Deep learning (Machine learning) Arafin, Irfana Islam, Md Mahirul Tazwar, Syed Ittisaf Das, Nilay Shuvra Anika, Sabrina Tabassum Application of CNN based architectures in detection of distracted drivers
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Bin Ashraf, Faisal
author_facet	Bin Ashraf, Faisal Arafin, Irfana Islam, Md Mahirul Tazwar, Syed Ittisaf Das, Nilay Shuvra Anika, Sabrina Tabassum
format	Thesis
author	Arafin, Irfana Islam, Md Mahirul Tazwar, Syed Ittisaf Das, Nilay Shuvra Anika, Sabrina Tabassum
author_sort	Arafin, Irfana
title	Application of CNN based architectures in detection of distracted drivers
title_short	Application of CNN based architectures in detection of distracted drivers
title_full	Application of CNN based architectures in detection of distracted drivers
title_fullStr	Application of CNN based architectures in detection of distracted drivers
title_full_unstemmed	Application of CNN based architectures in detection of distracted drivers
title_sort	application of cnn based architectures in detection of distracted drivers
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/21924
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Application of CNN based architectures in detection of distracted drivers

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