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Unusual Driver Behavior Detection in Videos Using Deep Learning Models

Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to...

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Autores principales: Abosaq, Hamad Ali, Ramzan, Muhammad, Althobiani, Faisal, Abid, Adnan, Aamir, Khalid Mahmood, Abdushkour, Hesham, Irfan, Muhammad, Gommosani, Mohammad E., Ghonaim, Saleh Mohammed, Shamji, V. R., Rahman, Saifur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823921/
https://www.ncbi.nlm.nih.gov/pubmed/36616911
http://dx.doi.org/10.3390/s23010311
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author Abosaq, Hamad Ali
Ramzan, Muhammad
Althobiani, Faisal
Abid, Adnan
Aamir, Khalid Mahmood
Abdushkour, Hesham
Irfan, Muhammad
Gommosani, Mohammad E.
Ghonaim, Saleh Mohammed
Shamji, V. R.
Rahman, Saifur
author_facet Abosaq, Hamad Ali
Ramzan, Muhammad
Althobiani, Faisal
Abid, Adnan
Aamir, Khalid Mahmood
Abdushkour, Hesham
Irfan, Muhammad
Gommosani, Mohammad E.
Ghonaim, Saleh Mohammed
Shamji, V. R.
Rahman, Saifur
author_sort Abosaq, Hamad Ali
collection PubMed
description Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior.
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spelling pubmed-98239212023-01-08 Unusual Driver Behavior Detection in Videos Using Deep Learning Models Abosaq, Hamad Ali Ramzan, Muhammad Althobiani, Faisal Abid, Adnan Aamir, Khalid Mahmood Abdushkour, Hesham Irfan, Muhammad Gommosani, Mohammad E. Ghonaim, Saleh Mohammed Shamji, V. R. Rahman, Saifur Sensors (Basel) Article Anomalous driving behavior detection is becoming more popular since it is vital in ensuring the safety of drivers and passengers in vehicles. Road accidents happen for various reasons, including health, mental stress, and fatigue. It is critical to monitor abnormal driving behaviors in real time to improve driving safety, raise driver awareness of their driving patterns, and minimize future road accidents. Many symptoms appear to show this condition in the driver, such as facial expressions or abnormal actions. The abnormal activity was among the most common causes of road accidents, accounting for nearly 20% of all accidents, according to international data on accident causes. To avoid serious consequences, abnormal driving behaviors must be identified and avoided. As it is difficult to monitor anyone continuously, automated detection of this condition is more effective and quicker. To increase drivers’ recognition of their driving behaviors and prevent potential accidents, a precise monitoring approach that detects abnormal driving behaviors and identifies abnormal driving behaviors is required. The most common activities performed by the driver while driving is drinking, eating, smoking, and calling. These types of driver activities are considered in this work, along with normal driving. This study proposed deep learning-based detection models for recognizing abnormal driver actions. This system is trained and tested using a newly created dataset, including five classes. The main classes include Driver-smoking, Driver-eating, Driver-drinking, Driver-calling, and Driver-normal. For the analysis of results, pre-trained and fine-tuned CNN models are considered. The proposed CNN-based model and pre-trained models ResNet101, VGG-16, VGG-19, and Inception-v3 are used. The results are compared by using the performance measures. The results are obtained 89%, 93%, 93%, 94% for pre-trained models and 95% by using the proposed CNN-based model. Our analysis and results revealed that our proposed CNN base model performed well and could effectively classify the driver’s abnormal behavior. MDPI 2022-12-28 /pmc/articles/PMC9823921/ /pubmed/36616911 http://dx.doi.org/10.3390/s23010311 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Abosaq, Hamad Ali
Ramzan, Muhammad
Althobiani, Faisal
Abid, Adnan
Aamir, Khalid Mahmood
Abdushkour, Hesham
Irfan, Muhammad
Gommosani, Mohammad E.
Ghonaim, Saleh Mohammed
Shamji, V. R.
Rahman, Saifur
Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title_full Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title_fullStr Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title_full_unstemmed Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title_short Unusual Driver Behavior Detection in Videos Using Deep Learning Models
title_sort unusual driver behavior detection in videos using deep learning models
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9823921/
https://www.ncbi.nlm.nih.gov/pubmed/36616911
http://dx.doi.org/10.3390/s23010311
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