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HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the D...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914727/ https://www.ncbi.nlm.nih.gov/pubmed/35271011 http://dx.doi.org/10.3390/s22051864 |
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author | Alkinani, Monagi H. Khan, Wazir Zada Arshad, Quratulain Raza, Mudassar |
author_facet | Alkinani, Monagi H. Khan, Wazir Zada Arshad, Quratulain Raza, Mudassar |
author_sort | Alkinani, Monagi H. |
collection | PubMed |
description | Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics. |
format | Online Article Text |
id | pubmed-8914727 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89147272022-03-12 HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features Alkinani, Monagi H. Khan, Wazir Zada Arshad, Quratulain Raza, Mudassar Sensors (Basel) Article Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features. With the goal to improve transportation safety and to reduce fatal accidents on roads, this research article presents a Hybrid Scheme for the Detection of Distracted Driving called HSDDD. This scheme is based on a strategy of aggregating handcrafted and deep CNN features. HSDDD is based on three-tiered architecture. The three tiers are named as Coordination tier, Concatenation tier and Classification tier. We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN features. DCNN extracted features are fused with HOG extracted features at the Concatenation tier. Then PCA is used as a feature selection technique. PCA takes both the extracted features and removes the redundant and irrelevant information, and it improves the classification performance. After feature fusion and feature selection, the two classifiers, KNN and SVM, at the Classification tier take the selected features and classify the ten classes of distracted driving behaviors. We evaluate our proposed scheme and observe its performance by using the accuracy metrics. MDPI 2022-02-26 /pmc/articles/PMC8914727/ /pubmed/35271011 http://dx.doi.org/10.3390/s22051864 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 Alkinani, Monagi H. Khan, Wazir Zada Arshad, Quratulain Raza, Mudassar HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title | HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title_full | HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title_fullStr | HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title_full_unstemmed | HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title_short | HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features |
title_sort | hsddd: a hybrid scheme for the detection of distracted driving through fusion of deep learning and handcrafted features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914727/ https://www.ncbi.nlm.nih.gov/pubmed/35271011 http://dx.doi.org/10.3390/s22051864 |
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