Domain adaptation for driver's gaze mapping for different drivers and new environments
Distracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2024-02-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | http://ijain.org/index.php/IJAIN/article/view/1168 |
| _version_ | 1827323341747257344 |
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| author | Ulziibayar Sonom-Ochir Stephen Karungaru Kenji Terada Altangerel Ayush |
| author_facet | Ulziibayar Sonom-Ochir Stephen Karungaru Kenji Terada Altangerel Ayush |
| author_sort | Ulziibayar Sonom-Ochir |
| collection | DOAJ |
| description | Distracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has shown promising results, but it faces challenges when applied to different drivers and environments. In this paper, we propose a domain adaptation-based solution for gaze mapping, which aims to accurately estimate a driver's gaze in diverse drivers and new environments. Our method consists of three steps: pre-processing, facial feature extraction, and gaze region classification. We explore two strategies for input feature extraction, one utilizing the full appearance of the driver and environment and the other focusing on the driver's face. Through unsupervised domain adaptation, we align the feature distributions of the source and target domains using a conditional Generative Adversarial Network (GAN). We conduct experiments on the Driver Gaze Mapping (DGM) dataset and the Columbia Cave-DB dataset to evaluate the performance of our method. The results demonstrate that our proposed method reduces the gaze mapping error, achieves better performance on different drivers and camera positions, and outperforms existing methods. We achieved an average Strictly Correct Estimation Rate (SCER) accuracy of 81.38% and 93.53% and Loosely Correct Estimation Rate (LCER) accuracy of 96.69% and 98.9% for the two strategies, respectively, indicating the effectiveness of our approach in adapting to different domains and camera positions. Our study contributes to the advancement of gaze mapping techniques and provides insights for improving driver safety in various driving scenarios. |
| first_indexed | 2024-04-25T01:43:24Z |
| format | Article |
| id | doaj.art-9ad438e341534ceb808acb31975e3b62 |
| institution | Directory Open Access Journal |
| issn | 2442-6571 2548-3161 |
| language | English |
| last_indexed | 2024-04-25T01:43:24Z |
| publishDate | 2024-02-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj.art-9ad438e341534ceb808acb31975e3b622024-03-08T03:14:05ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612024-02-011019410810.26555/ijain.v10i1.1168283Domain adaptation for driver's gaze mapping for different drivers and new environmentsUlziibayar Sonom-Ochir0Stephen Karungaru1Kenji Terada2Altangerel Ayush3Department of Information Science and Intelligent Systems, Tokushima UniversityDepartment of Information Science and Intelligent Systems,Tokushima UniversityDepartment of Information Science and Intelligent Systems,Tokushima UniversityDepartment of Information Technology, Mongolian University of Science and TechnologyDistracted driving is a leading cause of traffic accidents, and often arises from a lack of visual attention on the road. To enhance road safety, monitoring a driver's visual attention is crucial. Appearance-based gaze estimation using deep learning and Convolutional Neural Networks (CNN) has shown promising results, but it faces challenges when applied to different drivers and environments. In this paper, we propose a domain adaptation-based solution for gaze mapping, which aims to accurately estimate a driver's gaze in diverse drivers and new environments. Our method consists of three steps: pre-processing, facial feature extraction, and gaze region classification. We explore two strategies for input feature extraction, one utilizing the full appearance of the driver and environment and the other focusing on the driver's face. Through unsupervised domain adaptation, we align the feature distributions of the source and target domains using a conditional Generative Adversarial Network (GAN). We conduct experiments on the Driver Gaze Mapping (DGM) dataset and the Columbia Cave-DB dataset to evaluate the performance of our method. The results demonstrate that our proposed method reduces the gaze mapping error, achieves better performance on different drivers and camera positions, and outperforms existing methods. We achieved an average Strictly Correct Estimation Rate (SCER) accuracy of 81.38% and 93.53% and Loosely Correct Estimation Rate (LCER) accuracy of 96.69% and 98.9% for the two strategies, respectively, indicating the effectiveness of our approach in adapting to different domains and camera positions. Our study contributes to the advancement of gaze mapping techniques and provides insights for improving driver safety in various driving scenarios.http://ijain.org/index.php/IJAIN/article/view/1168gaze mappingdomain adaptationvisual attentiongaze regions |
| spellingShingle | Ulziibayar Sonom-Ochir Stephen Karungaru Kenji Terada Altangerel Ayush Domain adaptation for driver's gaze mapping for different drivers and new environments IJAIN (International Journal of Advances in Intelligent Informatics) gaze mapping domain adaptation visual attention gaze regions |
| title | Domain adaptation for driver's gaze mapping for different drivers and new environments |
| title_full | Domain adaptation for driver's gaze mapping for different drivers and new environments |
| title_fullStr | Domain adaptation for driver's gaze mapping for different drivers and new environments |
| title_full_unstemmed | Domain adaptation for driver's gaze mapping for different drivers and new environments |
| title_short | Domain adaptation for driver's gaze mapping for different drivers and new environments |
| title_sort | domain adaptation for driver s gaze mapping for different drivers and new environments |
| topic | gaze mapping domain adaptation visual attention gaze regions |
| url | http://ijain.org/index.php/IJAIN/article/view/1168 |
| work_keys_str_mv | AT ulziibayarsonomochir domainadaptationfordriversgazemappingfordifferentdriversandnewenvironments AT stephenkarungaru domainadaptationfordriversgazemappingfordifferentdriversandnewenvironments AT kenjiterada domainadaptationfordriversgazemappingfordifferentdriversandnewenvironments AT altangerelayush domainadaptationfordriversgazemappingfordifferentdriversandnewenvironments |