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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 inmodern vehicle design.
List of contents
Vision-Based Driver-Assistance Systems.- Driver-Environment Understanding.- Computer Vision Basics.- Object Detection, Classification, and Tracking.- Driver Drowsiness Detection.- Driver Inattention Detection.- Vehicle Detection and Distance Estimation.- Fuzzy Fusion for Collision Avoidance.
About the author
Mahdi Rezaei is Assistant Professor at Qazvin Islamic Azad University, Iran, and Honorary Academic Staff at the University of Auckland, New Zealand. He has a PhD in Computer Science and was awarded the Best Thesis Award from the University of Auckland. His research interests include computer vision, pattern recognition, and advanced driver assistance systems. Rezaei is the author of numerous contributions to top publications, including
IEEE Transactions on Intelligent Transportation Systems and
IEEE Conference on Computer Vision and Pattern Recognition, CVPR.
Reinhard Klette, Fellow of the Royal Society of New Zealand, is Professor at the Auckland University of Technology, New Zealand. He previously held positions at the University of Auckland, the Technical University of Berlin, and the Academy of Sciences Berlin. His research interests include computer vision, pattern recognition, and algorithm design. From 2003 to 2008, he was Associate Editor of
IEEE Transactions on Pattern Analysis and Machine Intelligence.
Summary
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 inmodern vehicle design.
Additional text
“This book is a first as it integrates the concepts and techniques of computer vision with those of ADAS. Practitioners, professors, and graduate students will appreciate the breath of the topics covered, the depth of analysis provided by the experiments, and the accessible writing style adopted by the authors.” (Computing Reviews, December, 2017)
Report
"This book is a first as it integrates the concepts and techniques of computer vision with those of ADAS. Practitioners, professors, and graduate students will appreciate the breath of the topics covered, the depth of analysis provided by the experiments, and the accessible writing style adopted by the authors." (Computing Reviews, December, 2017)
