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DETECTION OF DRIVER’S VISUAL DISTRACTION USING DUAL CAMERAS

https://tokushima-u.repo.nii.ac.jp/records/2010842
https://tokushima-u.repo.nii.ac.jp/records/2010842
9a5acb25-76f2-4381-9cad-5e745d7eb1cc
名前 / ファイル ライセンス アクション
ijicic_18_5_1445.pdf ijicic_18_5_1445.pdf (6.79 MB)
Item type 文献 / Documents(1)
公開日 2023-03-15
アクセス権
アクセス権 open access
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/10.24507/ijicic.18.05.1445
関連名称 10.24507/ijicic.18.05.1445
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 DETECTION OF DRIVER’S VISUAL DISTRACTION USING DUAL CAMERAS
著者 Sonom-Ochir, Ulziibayar

× Sonom-Ochir, Ulziibayar

en Sonom-Ochir, Ulziibayar

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カルンガル, スティフィン ギディンシ

× カルンガル, スティフィン ギディンシ

WEKO 1240
徳島大学 教育研究者総覧 82302/profile-ja.html
e-Rad 70380110

ja カルンガル, スティフィン ギディンシ
ISNI

ja-Kana カルンガル, スティフィン ギディンシ

en Karungaru, Stephen Githinji

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寺田, 賢治

× 寺田, 賢治

WEKO 106
徳島大学 教育研究者総覧 10760/profile-ja.html
e-Rad 40274261

ja 寺田, 賢治
ISNI

ja-Kana テラダ, ケンジ

en Terada, Kenji

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Ayush, Altangerel

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en Ayush, Altangerel

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抄録
内容記述 Most serious accidents are caused by the driver’s visual distraction. Therefore, early detection of a driver’s visual distraction is very important. The detection system mostly used is the dashboard camera because it is cheap and convenient. However, some studies have focused on various methods using additional equipment such as vehicle-mounted devices, wearable devices, and specific cameras that are common. However, these proposals are expensive. Therefore, the main goal of our research is to create a low-cost, non-intrusive, and lightweight driver’s visual distraction detection (DVDD) system using only a simple dual dashboard camera. Currently, most research has focused only on tracking and estimating the driver’s gaze. In our study, additionally, we also aim to monitor the road environment and then evaluate the driver’s visual distraction detection based on the two pieces of information. The proposed system has two main modules: 1) gaze mapping and 2) moving object detection. The gaze mapping module receives video captured through a camera placed in front of the driver, and then predicts a driver’s gaze direction to one of predefined 16 gaze regions. Concurrently, the moving object detection module identifies the moving objects from the front view and determines in which part of the predefined 16 gaze regions it appears. By combining and evaluating the two modules, the state of the distraction of the driver can be estimated. If the two module outputs are different gaze regions or non-neighbor gaze regions, the system considers that the driver is visually distracted and issues a warning. We conducted experiments based on our self-built real-driving DriverGazeMapping dataset. In the gaze mapping module, we compared the two methods MobileNet and OpenFace with the SVM classifier. The two methods outperformed the baseline gaze mapping module. Moreover, in the OpenFace with SVM classifier method, we investigated which features extracted by OpenFace affected the performance of the gaze mapping module. Of these, the most effective feature was the combination of a gaze angle and head position_R features. The OpenFace with SVM method using gaze angle and head position_R features achieved a 6.25% higher accuracy than the method using MobileNet. Besides, the moving object detection module using the Lukas-Kanade dense method was faster and more reliable than in the previous study in our experiments.
キーワード
主題 Visual distraction
キーワード
主題 Gaze mapping
キーワード
主題 Moving object
キーワード
主題 Gaze region
書誌情報 en : International Journal of Innovative Computing, Information and Control

巻 18, 号 5, p. 1445-1461, 発行日 2022-10
収録物ID
収録物識別子タイプ ISSN
収録物識別子 13494198
収録物ID
収録物識別子タイプ NCID
収録物識別子 AA12218449
出版者
出版者 ICIC International
EID
識別子 387292
言語
言語 eng
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