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Using Image Processing in the Proposed Drowsiness Detection System Design
BACKGROUND: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Tehran University of Medical Sciences
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174048/ https://www.ncbi.nlm.nih.gov/pubmed/30320012 |
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author | POURSADEGHIYAN, Mohsen MAZLOUMI, Adel NASL SARAJI, Gebraeil BANESHI, Mohammad Mehdi KHAMMAR, Alireza EBRAHIMI, Mohammad Hossein |
author_facet | POURSADEGHIYAN, Mohsen MAZLOUMI, Adel NASL SARAJI, Gebraeil BANESHI, Mohammad Mehdi KHAMMAR, Alireza EBRAHIMI, Mohammad Hossein |
author_sort | POURSADEGHIYAN, Mohsen |
collection | PubMed |
description | BACKGROUND: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. METHODS: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. RESULTS: Eye closure duration and blink frequency have a direct ratio of drivers’ levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. CONCLUSION: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ drowsiness by considering the dynamic changes of the eyes. |
format | Online Article Text |
id | pubmed-6174048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Tehran University of Medical Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-61740482018-10-12 Using Image Processing in the Proposed Drowsiness Detection System Design POURSADEGHIYAN, Mohsen MAZLOUMI, Adel NASL SARAJI, Gebraeil BANESHI, Mohammad Mehdi KHAMMAR, Alireza EBRAHIMI, Mohammad Hossein Iran J Public Health Original Article BACKGROUND: Drowsiness is one of the underlying causes of driving accidents, which contribute, to many road fatalities annually. Although numerous methods have been developed to detect the level of drowsiness, techniques based on image processing are quicker and more accurate in comparison with the other methods. The aim of this study was to use image-processing techniques to detect the levels of drowsiness in a driving simulator. METHODS: This study was conducted on five suburban drivers using a driving simulator based on virtual reality laboratory of Khaje-Nasir Toosi University of Technology in 2015 Tehran, Iran. The facial expressions, as well as location of the eyes, were detected by Violla-Jones algorithm. Criteria for detecting drivers’ levels of drowsiness by eyes tracking included eye blink duration blink frequency and PERCLOS that was used to confirm the results. RESULTS: Eye closure duration and blink frequency have a direct ratio of drivers’ levels of drowsiness. The mean of squares of errors for data trained by the network and data into the network for testing, were 0.0623 and 0.0700, respectively. Meanwhile, the percentage of accuracy of detecting system was 93. CONCLUSION: The results showed several dynamic changes of the eyes during the periods of drowsiness. The present study proposes a fast and accurate method for detecting the levels of drivers’ drowsiness by considering the dynamic changes of the eyes. Tehran University of Medical Sciences 2018-09 /pmc/articles/PMC6174048/ /pubmed/30320012 Text en Copyright© Iranian Public Health Association & Tehran University of Medical Sciences http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article POURSADEGHIYAN, Mohsen MAZLOUMI, Adel NASL SARAJI, Gebraeil BANESHI, Mohammad Mehdi KHAMMAR, Alireza EBRAHIMI, Mohammad Hossein Using Image Processing in the Proposed Drowsiness Detection System Design |
title | Using Image Processing in the Proposed Drowsiness Detection System Design |
title_full | Using Image Processing in the Proposed Drowsiness Detection System Design |
title_fullStr | Using Image Processing in the Proposed Drowsiness Detection System Design |
title_full_unstemmed | Using Image Processing in the Proposed Drowsiness Detection System Design |
title_short | Using Image Processing in the Proposed Drowsiness Detection System Design |
title_sort | using image processing in the proposed drowsiness detection system design |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174048/ https://www.ncbi.nlm.nih.gov/pubmed/30320012 |
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