Rezaei, M orcid.org/0000-0003-3892-421X and Klette, R (2017) Computer Vision for Driver Assistance. Computational Imaging and Vision, 45 . Springer International Publishing ISBN 9783319505497
Abstract
This book summarises the state of the art in computer vision-based driver and road monitoring, focussing on monocular vision technology in particular, with the aim to address challenges of driver assistance and autonomous driving systems.
While the systems designed for the assistance of drivers of on-road vehicles are currently converging to the design of autonomous vehicles, the research presented here focuses on scenarios where a driver is still assumed to pay attention to the traffic while operating a partially automated vehicle. Proposing various computer vision algorithms, techniques and methodologies, the authors also provide a general review of computer vision technologies that are relevant for driver assistance and fully autonomous vehicles.
Computer Vision for Driver Assistance is the first book of its kind and will appeal to undergraduate and graduate students, researchers, engineers and those generally interested in computer vision-related topics in modern vehicle design.
Metadata
Item Type: | Book |
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Authors/Creators: |
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Keywords: | advanced driver-assistance systems; autonomous vehicles; driver distraction; driver fatigue; vehicle detection; supervised learning; unsupervised learning; object detection; object tracking; fuzzy logic |
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 Sep 2020 11:14 |
Last Modified: | 24 Nov 2020 10:32 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Computational Imaging and Vision |
Identification Number: | 10.1007/978-3-319-50551-0 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:165117 |