Rezaei, M orcid.org/0000-0003-3892-421X and Klette, R (2011) Simultaneous analysis of driver behaviour and road condition for driver distraction detection. International Journal of Image and Data Fusion, 2 (3). pp. 217-236. ISSN 1947-9832
Abstract
The design of intelligent driver assistance systems is of increasing importance for the vehicle-producing industry and road-safety solutions. This article starts with a review of road-situation monitoring and driver's behaviour analysis. This article also discusses lane tracking using vision (or other) sensors, and the strength or weakness of different methods of driver behaviour analysis (e.g. iris or pupil status monitoring, and EEG spectrum analysis). This article focuses then on image analysis techniques and develops a multi-faceted approach in order to analyse driver's face and eye status via implementing a real-time AdaBoost cascade classifier with Haar-like features. The proposed method is tested in a research vehicle for driver distraction detection using a binocular camera. The developed algorithm is robust in detecting different types of driver distraction such as drowsiness, fatigue, drunk driving or the performance of secondary tasks.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2011 Taylor & Francis. This is an author produced version of an article published in International Journal of Image and Data Fusion. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | driver assistance systems; driver distraction detection; Haar-like features; cascaded classifier; face and eye detection |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 15 Dec 2022 15:04 |
Last Modified: | 16 Dec 2022 20:31 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/19479832.2011.590458 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157588 |