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Domain adaptation for driver's gaze mapping for different drivers and new environments
https://tokushima-u.repo.nii.ac.jp/records/2000306
https://tokushima-u.repo.nii.ac.jp/records/2000306d226ddc1-fff4-4d31-b103-e566ed839904
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
ijain_10_1_94.pdf (1.2 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||
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| 公開日 | 2024-11-15 | |||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.26555/ijain.v10i1.1168 | |||||||||||||||||||||
| 関連名称 | 10.26555/ijain.v10i1.1168 | |||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||
| タイトル | ||||||||||||||||||||||
| タイトル | Domain adaptation for driver's gaze mapping for different drivers and new environments | |||||||||||||||||||||
| 著者 |
Sonom-Ochir, Ulziibayar
× Sonom-Ochir, Ulziibayar
× カルンガル, スティフィン ギディンシ
WEKO
1240
× 寺田, 賢治
WEKO
106
× Ayush, Altangerel
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| 抄録 | ||||||||||||||||||||||
| 内容記述 | 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. | |||||||||||||||||||||
| キーワード | ||||||||||||||||||||||
| 主題 | Gaze mapping | |||||||||||||||||||||
| キーワード | ||||||||||||||||||||||
| 主題 | Domain adaptation | |||||||||||||||||||||
| キーワード | ||||||||||||||||||||||
| 主題 | Visual attention | |||||||||||||||||||||
| キーワード | ||||||||||||||||||||||
| 主題 | Gaze regions | |||||||||||||||||||||
| 書誌情報 |
en : International Journal of Advances in Intelligent Informatics 巻 10, 号 1, p. 94-108, 発行日 2024-02-29 |
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| 収録物識別子タイプ | PISSN | |||||||||||||||||||||
| 収録物識別子 | 24426571 | |||||||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||||
| 収録物識別子 | 25483161 | |||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||
| 出版者 | Universitas Ahmad Dahlan | |||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||
| 権利情報 | This is an open access article under the CC–BY-SA license. | |||||||||||||||||||||
| EID | ||||||||||||||||||||||
| 識別子 | 405091 | |||||||||||||||||||||
| 言語 | ||||||||||||||||||||||
| 言語 | eng | |||||||||||||||||||||
