© 2017

Computer Vision for Driver Assistance

Simultaneous Traffic and Driver Monitoring

  • 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


Part of the Computational Imaging and Vision book series (CIVI, volume 45)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Mahdi Rezaei, Reinhard Klette
    Pages 1-18
  3. Mahdi Rezaei, Reinhard Klette
    Pages 19-36
  4. Mahdi Rezaei, Reinhard Klette
    Pages 37-49
  5. Mahdi Rezaei, Reinhard Klette
    Pages 51-94
  6. Mahdi Rezaei, Reinhard Klette
    Pages 95-126
  7. Mahdi Rezaei, Reinhard Klette
    Pages 127-145
  8. Mahdi Rezaei, Reinhard Klette
    Pages 147-188
  9. Mahdi Rezaei, Reinhard Klette
    Pages 189-205
  10. Back Matter
    Pages 207-224

About this book


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. 


advanced driver-assistance systems autonomous vehicles driver distraction driver fatigue vehicle detection supervised learning unsupervised learning object detection object tracking fuzzy logic

Authors and affiliations

  1. 1.Department of Computer EngineeringQazvin Islamic Azad University QazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of Technology AucklandNew Zealand

About the authors

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.

Bibliographic information


“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)