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A Driver’s Visual Attention Prediction Using Optical Flow
Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in compute...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198310/ https://www.ncbi.nlm.nih.gov/pubmed/34071901 http://dx.doi.org/10.3390/s21113722 |
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author | Kang, Byeongkeun Lee, Yeejin |
author_facet | Kang, Byeongkeun Lee, Yeejin |
author_sort | Kang, Byeongkeun |
collection | PubMed |
description | Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features. |
format | Online Article Text |
id | pubmed-8198310 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81983102021-06-14 A Driver’s Visual Attention Prediction Using Optical Flow Kang, Byeongkeun Lee, Yeejin Sensors (Basel) Article Motion in videos refers to the pattern of the apparent movement of objects, surfaces, and edges over image sequences caused by the relative movement between a camera and a scene. Motion, as well as scene appearance, are essential features to estimate a driver’s visual attention allocation in computer vision. However, the fact that motion can be a crucial factor in a driver’s attention estimation has not been thoroughly studied in the literature, although driver’s attention prediction models focusing on scene appearance have been well studied. Therefore, in this work, we investigate the usefulness of motion information in estimating a driver’s visual attention. To analyze the effectiveness of motion information, we develop a deep neural network framework that provides attention locations and attention levels using optical flow maps, which represent the movements of contents in videos. We validate the performance of the proposed motion-based prediction model by comparing it to the performance of the current state-of-art prediction models using RGB frames. The experimental results for a real-world dataset confirm our hypothesis that motion plays a role in prediction accuracy improvement, and there is a margin for accuracy improvement by using motion features. MDPI 2021-05-27 /pmc/articles/PMC8198310/ /pubmed/34071901 http://dx.doi.org/10.3390/s21113722 Text en © 2021 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 Kang, Byeongkeun Lee, Yeejin A Driver’s Visual Attention Prediction Using Optical Flow |
title | A Driver’s Visual Attention Prediction Using Optical Flow |
title_full | A Driver’s Visual Attention Prediction Using Optical Flow |
title_fullStr | A Driver’s Visual Attention Prediction Using Optical Flow |
title_full_unstemmed | A Driver’s Visual Attention Prediction Using Optical Flow |
title_short | A Driver’s Visual Attention Prediction Using Optical Flow |
title_sort | driver’s visual attention prediction using optical flow |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8198310/ https://www.ncbi.nlm.nih.gov/pubmed/34071901 http://dx.doi.org/10.3390/s21113722 |
work_keys_str_mv | AT kangbyeongkeun adriversvisualattentionpredictionusingopticalflow AT leeyeejin adriversvisualattentionpredictionusingopticalflow AT kangbyeongkeun driversvisualattentionpredictionusingopticalflow AT leeyeejin driversvisualattentionpredictionusingopticalflow |