Details

Computer Vision for Driver Assistance

Simultaneous Traffic and Driver Monitoring
Computational Imaging and Vision, Band 45

von: Mahdi Rezaei, Reinhard Klette
128,39 €
Verlag:	Springer
Format:	PDF
Veröffentl.:	06.02.2017
ISBN/EAN:	9783319505510
Sprache:	englisch

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Beschreibungen

Titelbeschreibung

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.

Inhaltsverzeichnis

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.

Autorenportrait

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.

Back cover copy

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.

Sonderbeitrag

Explores theoretical and practical aspects of driver assistance systems, illustrated by various real-world experiments Presents technically advanced concepts in an accessible way that is suitable for beginners in the field Provides comprehensive guidelines for students, researchers and practitioners Elaborates on current topics in computer vision and pattern recognition, such as object detection, object tracking, and supervised and unsupervised classification techniques