Driver Drowsiness Detection (eBook)

Systems and Solutions

Aleksandar Čolić, Oge Marques, Borko Furht (Autoren)

eBook Download: PDF

2014
50 Seiten
Springer International Publishing (Verlag)
978-3-319-11535-1 (ISBN)

Infos

This SpringerBrief presents the fundamentals of driver drowsiness detection systems, provides examples of existing products, and offers guides for practitioners interested in developing their own solutions to the problem. Driver drowsiness causes approximately 7% of all road accidents and up to 18% of fatal collisions. Proactive systems that are capable of preventing the loss of lives combine techniques, methods, and algorithms from many fields of engineering and computer science such as sensor design, image processing, computer vision, mobile application development, and machine learning which is covered in this brief. The major concepts addressed in this brief are: the need for such systems, the different methods by which drowsiness can be detected (and the associated terminology), existing commercial solutions, selected algorithms and research directions, and a collection of examples and case studies. These topics equip the reader to understand this critical field and its applications. Detection Systems and Solutions: Driver Drowsiness is an invaluable resource for researchers and professionals working in intelligent vehicle systems and technologies. Advanced-level students studying computer science and electrical engineering will also find the content helpful.

Inhalt

Preface 8
Contents 10
Chapter 1 Introduction 11
1.1 The Problem of Driver Drowsiness 11
1.2 What Is Drowsiness? 12
1.3 What Causes Drowsiness? 14
1.4 What Can We Do About It? 15
References 15
Chapter 2 Driver Drowsiness Detection and Measurement Methods 16
2.1 Subjective Methods 16
2.2 Physiological Methods 19
2.3 Vehicle-Based Methods 20
2.4 Behavioral Methods 22
2.5 Hybrid Methods 23
References 23
Chapter 3 Commercial Solutions 28
3.1 Car Manufacturers 28
3.2 Independent Products 31
References 32
Chapter 4 Research Aspects 33
4.1 Imaging Sensors 34
4.1.1 Visible Light Cameras 34
4.1.2 Near Infrared (NIR) Cameras 35
4.2 Feature Detection and Extraction 35
4.3 Machine Learning Classifiers 40
4.4 Challenges and Practical Aspects 42
4.4.1 Data Collection 42
4.4.2 Performance Requirements 43
References 44
Chapter 5 Examples 46
5.1 Selected Recent Work 46
5.1.1 Head Pose Estimation 47
5.1.2 Yawning 47
5.1.3 Eye State Estimation 48
5.2 Our Work 50
5.2.1 System Initialization: Preparation 51
Skin Color Feature Analysis 53
Eye Model Analysis 54
Head Position Analysis 54
5.2.2 Regular Stage: Eye Tracking with Eye-State Analysis 54
5.2.3 Warning Stage: Nod Analysis 55
Head Position Monitoring 55
5.2.4 Alert Stage 56
References 57

Produkt-Details

• Erscheint lt. Verlag: 27.9.2014
• Reihe/Serie: SpringerBriefs in Computer Science
• Reihe/Serie: SpringerBriefs in Computer Science
• Zusatzinfo: IX, 50 p. 16 illus., 15 illus. in color.
• Verlagsort: Cham
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik ► Grafik / Design
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: Computer vision applications • Detection systems • Driver Drowsiness • Intelligent Systems • Intelligent vehicle technologies • Machine vision applications
• ISBN-10: 3-319-11535-9 / 3319115359
• ISBN-13: 978-3-319-11535-1 / 9783319115351