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Computer vision for driver assistance : simultaneous traffic and driver monitoring
Rezaei M., Klette R., Springer International Publishing, New York, NY, 2017. 224 pp. Type: Book (978-3-319505-49-7)
Date Reviewed: Dec 15 2017

This new book by Rezaei and Klette is the first of its kind, as it puts computer vision techniques in the context of advanced driving assistance systems (ADAS).

Driver assistance systems are those that provide help to drivers, such as stability and traction control, adaptive cruise control, lane keeping assistance, and driver fatigue detection, among others. Until relatively recently, these systems did not directly include the vehicle driver as an element of safety in relation to vehicle dynamics and the traffic context in which the driving occurs. This is now about to change as much research effort is deployed to understand the relationship that exists between the behavioral elements of drivers and their vehicular environments.

The automobile industry is undoubtedly going through a revolution, and this timely book sheds light on a subset of the techniques involved in such rapid technological changes. The intended audience for this book is composed of people involved with adapting vision techniques to the task of driving, from engineers and computer scientists to professors in the field and their graduate students. Although a small section of the book is devoted to stereo sensing, the thematic focus is primarily on monocular vision and its techniques as they are presented in the context of their integration with navigational tasks involved in driving road vehicles.

Much more than just a survey, the book provides a number of novel contributions to the field. Among them, a refined training step method is offered for cascades of classifiers, along with the presentation of novel classifiers that improve on detection rates and are robust in the presence of noise. New techniques are also introduced that minimize the effects of light intensity variations affecting scene detection and reconstruction.

More importantly, through its chapters the book brings the reader to its meaningful and relevant conclusion, which is an understanding of the relationship between the environment of a vehicle and the behavioral parameters of its driver.

The first chapters emphasize detection and understanding of the elements contained in monocular road scenes, followed by a focus on driver behavioral parameters, such as inattention, fatigue, and gaze behavior. These topics merge in the last chapter of the book to form a successful attempt at relating driver gaze direction (a behavioral element) to obstacles on the roadway (elements of scene understanding), in the context of a collision-warning set of techniques.

The book is well structured and balanced in its approach to the technical elements involved. Throughout all chapters, algorithms and experiments are provided that support the authors’ arguments and conclusions. The bibliography is rather complete and spans an impressive set of topics from ADAS to computer vision to machine learning, and more. However, the book would benefit from including a chapter on deep learning as many successful contributions have been brought by modern artificial intelligence (AI) techniques to the field of ADAS and autonomous driving in the last few years.

To conclude, 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.

Reviewer:  Steven S. Beauchemin Review #: CR145714 (1802-0057)
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Vision And Scene Understanding (I.2.10 )
 
 
Computer Vision (I.5.4 ... )
 
 
Real-Time And Embedded Systems (C.3 ... )
 
 
Special-Purpose And Application-Based Systems (C.3 )
 
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