Computer Vision in Vehicle Technology

Land, Sea, and Air
Herausgegeben:López, Antonio M.; Imiya, Atsushi; Pajdla, Tomas; Álvarez, Jose M.

• Gebundenes Buch

Produktbeschreibung

A unified view of the use of computer vision technology for different types of vehicles

Computer Vision in Vehicle Technology focuses on computer vision as on-board technology, bringing together fields of research where computer vision is progressively penetrating: the automotive sector, unmanned aerial and underwater vehicles. It also serves as a reference for researchers of current developments and challenges in areas of the application of computer vision, involving vehicles such as advanced driver assistance (pedestrian detection, lane departure warning, traffic sign recognition), autonomous driving and robot navigation (with visual simultaneous localization and mapping) or unmanned aerial vehicles (obstacle avoidance, landscape classification and mapping, fire risk assessment).

The overall role of computer vision for the navigation of different vehicles, as well as technology to address on-board applications, is analysed.

Key features:
_ Presents the latest advances in the field of computer vision and vehicle technologies in a highly informative and understandable way, including the basic mathematics for each problem.
_ Provides a comprehensive summary of the state of the art computer vision techniques in vehicles from the navigation and the addressable applications points of view.
_ Offers a detailed description of the open challenges and business opportunities for the immediate future in the field of vision based vehicle technologies.

This is essential reading for computer vision researchers, as well as engineers working in vehicle technologies, and students of computer vision.

Produktdetails

• Verlag: Wiley / Wiley & Sons
• Artikelnr. des Verlages: 1W118868070
• 1. Auflage
• Seitenzahl: 216
• Erscheinungstermin: 17. April 2017
• Englisch
• Abmessung: 250mm x 175mm x 16mm
• Gewicht: 562g
• ISBN-13: 9781118868072
• ISBN-10: 1118868072
• Artikelnr.: 45558991

Autorenporträt

Dr. Antonio M. López is the head of the Advanced Driver Assistance Systems (ADAS) Group of the Computer Vision Center (CVC), and Associate Professor of the Computer Science Department, both from the Universitat Autònoma de Barcelona (UAB). Antonio received a BSc degree in Computer Science from the Universitat Politècnica de Catalunya (UPC) and a PhD degree in Computer Vision from the Universitat Autònoma de Barcelona (UAB). In 1996, he participated in the foundation of the CVC at the UAB, where he has held different institutional responsibilities. Antonio is also the responsible of the Software Engineering specialty at the UAB. Moreover, he has been the principal investigator of numerous public and industrial research projects, and is a co-author of more than 100 journal and conference papers, all in the field of computer vision. Antonio's main research interests are vision-based driver assistance and autonomous driving. Atsushi Imiya is Professor at IMIT, Chiba University. He has served as a PC member of DGCI, IWCIA, and SSVM conferences for many years. He is an editorial member of "Pattern Recognition (Journal)" and a co-editor of "Digital and Image Geometry" held at Schloss Dagstuhl in 2000, MLDM2007 (Machine Learning and Data Mining in Pattern Recognition), of which proceedings were published from Springer-Verlag. He is a general co-chair of S+SSPR (Statistical, and Synthetic and Structural Pattern Recognition) 2012. He is participating in a government-funded project titled: "Computational anatomy for computer-aided diagnosis and therapy: Frontiers of medical image sciences" as an applied mathematician. He also serves as a review committee of the research projects internationally. Dr. Tomas Pajdla is an Assistant Professor and Distinguished Senior Researcher at the Czech Technical University in Prague. He works in geometry and algebra of computer vision and robotics with the emphasis on geometry a calibration of camera systems, 3D reconstruction and industrial vision. Dr. Pajdla published more than 75 works in journals and proceedings and received awards for his work; OAGM 1998, 2012, BMVC 2002, ICCV 2005 and ACCV 2014. He has served as a program co-chair of ECCV 2004 and ECCV 2014, and regularly as area chair of ICCV, CVPR, ECCV, ACCV, ICRA and BMVC. He is a member of the ECCV Board, and served on the boards of IEEE PAMI, Computer Vision and Image Understanding and IPSJ Transactions on Computer Vision and Applications journals. Dr. Pajdla has connections to the planetary research community through EU projects with NASA, ESA and EADS Astrium and to automotive industry via Daimler AG. Jose M. Alvarez is currently a researcher at NICTA and a research fellow at the Australian National University, Canberra, Australia. Previously, he was a postdoctoral researcher at the Computational and Biological Learning Group at New York University with Professor Yann LeCun. During his Ph.D. he was a visiting researcher at the University of Amsterdam and Volkswagen AG research. His main research interests include deep learning and data driven methods for dynamic scene understanding.

Inhaltsangabe

List of Contributors ix

Preface xi

Abbreviations and Acronyms xiii

1 Computer Vision in Vehicles 1
Reinhard Klette

1.1 Adaptive Computer Vision for Vehicles 1

1.1.1 Applications 1

1.1.2 Traffic Safety and Comfort 2

1.1.3 Strengths of (Computer) Vision 2

1.1.4 Generic and Specific Tasks 3

1.1.5 Multi-module Solutions 4

1.1.6 Accuracy, Precision, and Robustness 5

1.1.7 Comparative Performance Evaluation 5

1.1.8 There Are Many Winners 6

1.2 Notation and Basic Definitions 6

1.2.1 Images and Videos 6

1.2.2 Cameras 8

1.2.3 Optimization 10

1.3 Visual Tasks 12

1.3.1 Distance 12

1.3.2 Motion 16

1.3.3 Object Detection and Tracking 18

1.3.4 Semantic Segmentation 21

1.4 Concluding Remarks 23

Acknowledgments 23

2 Autonomous Driving 24
Uwe Franke

2.1 Introduction 24

2.1.1 The Dream 24

2.1.2 Applications 25

2.1.3 Level of Automation 26

2.1.4 Important Research Projects 27

2.1.5 Outdoor Vision Challenges 30

2.2 Autonomous Driving in Cities 31

2.2.1 Localization 33

2.2.2 Stereo Vision-Based Perception in 3D 36

2.2.3 Object Recognition 43

2.3 Challenges 49

2.3.1 Increasing Robustness 49

2.3.2 Scene Labeling 50

2.3.3 Intention Recognition 52

2.4 Summary 52

Acknowledgments 54

3 Computer Vision for MAVs 55
Friedrich Fraundorfer

3.1 Introduction 55

3.2 System and Sensors 57

3.3 Ego-Motion Estimation 58

3.3.1 State Estimation Using Inertial and Vision Measurements 58

3.3.2 MAV Pose from Monocular Vision 62

3.3.3 MAV Pose from Stereo Vision 63

3.3.4 MAV Pose from Optical Flow Measurements 65

3.4 3D Mapping 67

3.5 Autonomous Navigation 71

3.6 Scene Interpretation 72

3.7 Concluding Remarks 73

4 Exploring the Seafloor with Underwater Robots 75
Rafael Garcia, Nuno Gracias, Tudor Nicosevici, Ricard Prados, Natalia Hurtos, Ricard Campos, Javier Escartin, Armagan Elibol, Ramon Hegedus and Laszlo Neumann

4.1 Introduction 75

4.2 Challenges of Underwater Imaging 77

4.3 Online Computer Vision Techniques 79

4.3.1 Dehazing 79

4.3.2 Visual Odometry 84

4.3.3 SLAM 87

4.3.4 Laser Scanning 91

4.4 Acoustic Imaging Techniques 92

4.4.1 Image Formation 92

4.4.2 Online Techniques for Acoustic Processing 95

4.5 Concluding Remarks 98

Acknowledgments 99

5 Vision-Based Advanced Driver Assistance Systems 100
David Gerónimo, David Vázquez and Arturo de la Escalera

5.1 Introduction 100

5.2 Forward Assistance 101

5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA) 101

5.2.2 Traffic Sign Recognition (TSR) 103

5.2.3 Traffic Jam Assist (TJA) 105

5.2.4 Vulnerable Road User Protection 106

5.2.5 Intelligent Headlamp Control 109

5.2.6 Enhanced Night Vision (Dynamic Light Spot) 110

5.2.7 Intelligent Active Suspension 111

5.3 Lateral Assistance 112

5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS) 112

5.3.2 Lane Change Assistance (LCA) 115

5.3.3 Parking Assistance 116

5.4 Inside Assistance 117

5.4.1 Driver Monitoring and Drowsiness Detection 117

5.5 Conclusions and Future Challenges 119
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