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E-raamat: Computer Vision in Vehicle Technology - Land, Sea, and Air: Land, Sea, and Air [Wiley Online]

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  • Formaat: 216 pages
  • Ilmumisaeg: 31-Mar-2017
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118868064
  • ISBN-13: 9781118868065
Teised raamatud teemal:
  • Wiley Online
  • Hind: 116,30 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Computer Vision in Vehicle Technology - Land, Sea, and Air: Land, Sea, and AirSuurem pilt
  • Formaat: 216 pages
  • Ilmumisaeg: 31-Mar-2017
  • Kirjastus: John Wiley & Sons Inc
  • ISBN-10: 1118868064
  • ISBN-13: 9781118868065
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118868065

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.

List of Contributors
ix
Preface xi
Abbreviations and Acronyms xiii
1 Computer Vision in Vehicles
1(23)
Reinhard Klette
1.1 Adaptive Computer Vision for Vehicles
1(5)
1.1.1 Applications
1(1)
1.1.2 Traffic Safety and Comfort
2(1)
1.1.3 Strengths of (Computer) Vision
2(1)
1.1.4 Generic and Specific Tasks
3(1)
7.7.5 Multi-module Solutions
4(1)
7.7.6 Accuracy, Precision, and Robustness
5(1)
1.1.7 Comparative Performance Evaluation
5(1)
1.1.8 There Are Many Winners
6(1)
1.2 Notation and Basic Definitions
6(6)
7.2.7 Images and Videos
6(2)
7.2.2 Cameras
8(2)
1.2.3 Optimization
10(2)
1.3 Visual Tasks
12(11)
1.3.1 Distance
12(4)
1.3.2 Motion
16(2)
1.3.3 Object Detection and Tracking
18(3)
1.3.4 Semantic Segmentation
21(2)
1.4 Concluding Remarks
23(1)
Acknowledgments
23(1)
2 Autonomous Driving
24(31)
Uwe Franke
2.1 Introduction
24(7)
2.1.1 The Dream
24(1)
2.1.2 Applications
25(1)
2.1.3 Level of Automation
26(1)
2.1.4 Important Research Projects
27(3)
2.7.5 Outdoor Vision Challenges
30(1)
2.2 Autonomous Driving in Cities
31(18)
2.2.1 Localization
33(3)
2.2.2 Stereo Vision-Based Perception in 3D
36(7)
2.2.3 Object Recognition
43(6)
2.3 Challenges
49(3)
2.3.1 Increasing Robustness
49(1)
2.3.2 Scene Labeling
50(2)
2.3.3 Intention Recognition
52(1)
2.4 Summary
52(3)
Acknowledgments
54(1)
3 Computer Vision for MAVs
55(20)
Friedrich Fraundorfer
3.1 Introduction
55(2)
3.2 System and Sensors
57(1)
3.3 Ego-Motion Estimation
58(9)
3.3.1 State Estimation Using Inertial and Vision Measurements
58(4)
3.3.2 MAV Pose from Monocular Vision
62(1)
3.3.3 MAV Pose from Stereo Vision
63(2)
3.3.4 MAV Pose from Optical Flow Measurements
65(2)
3.4 3D Mapping
67(4)
3.5 Autonomous Navigation
71(1)
3.6 Scene Interpretation
72(1)
3.7 Concluding Remarks
73(2)
4 Exploring the Seafloor with Underwater Robots
75(25)
Rafael Garcia
Nuno Gracias
Tudor Nicosevici
Ricard Prados
Natalia Hurtos
Ricard Campos
Javier Escartin
Armagan Elibol
Ramon Hegedus
Laszlo Neumann
4.1 Introduction
75(2)
4.2 Challenges of Underwater Imaging
77(2)
4.3 Online Computer Vision Techniques
79(13)
4.3.1 Dehazing
79(5)
4.3.2 Visual Odometry
84(3)
4.3.3 SLAM
87(4)
4.3.4 Laser Scanning
91(1)
4.4 Acoustic Imaging Techniques
92(6)
4.4.1 Image Formation
92(3)
4.4.2 Online Techniques for Acoustic Processing
95(3)
4.5 Concluding Remarks
98(2)
Acknowledgments
99(1)
5 Vision-Based Advanced Driver Assistance Systems
100(22)
David Geronimo
David Vazquez
Arturo de la Escalera
5.1 Introduction
100(1)
5.2 Forward Assistance
101(11)
5.2.1 Adaptive Cruise Control (ACC) and Forward Collision Avoidance (FCA)
101(2)
5.2.2 Traffic Sign Recognition (TSR)
103(2)
5.2.3 Traffic Jam Assist (TJA)
105(1)
5.2.4 Vulnerable Road User Protection
106(3)
5.2.5 Intelligent Headlamp Control
109(1)
5.2.6 Enhanced Night Vision (Dynamic Light Spot)
110(1)
5.2.7 Intelligent Active Suspension
111(1)
5.3 Lateral Assistance
112(5)
5.3.1 Lane Departure Warning (LDW) and Lane Keeping System (LKS)
112(3)
5.3.2 Lane Change Assistance (LCA)
115(1)
5.3.3 Parking Assistance
116(1)
5.4 Inside Assistance
117(2)
5.4.1 Driver Monitoring and Drowsiness Detection
117(2)
5.5 Conclusions and Future Challenges
119(3)
5.5.1 Robustness
119(2)
5.5.2 Cost
121(1)
Acknowledgments
121(1)
6 Application Challenges from a Bird's-Eye View
122(11)
Davide Scaramuzza
6.1 Introduction to Micro Aerial Vehicles (MAVs)
122(2)
6.1.1 Micro Aerial Vehicles (MAVs)
122(1)
6.1.2 Rotorcraft MAVs
123(1)
6.2 GPS-Denied Navigation
124(3)
6.2.1 Autonomous Navigation with Range Sensors
124(1)
6.2.2 Autonomous Navigation with Vision Sensors
125(1)
6.2.3 SFLY: Swarm of Micro Flying Robots
126(1)
6.2.4 SVO, a Visual-Odometry Algorithm for MAVs
126(1)
6.3 Applications and Challenges
127(5)
6.3.1 Applications
127(1)
6.3.2 Safety and Robustness
128(4)
6.4 Conclusions
132(1)
7 Application Challenges of Underwater Vision
133(28)
Nuno Gracias
Rafael Garcia
Ricard Campos
Natalia Hurtos
Ricard Prados
ASM Shihavuddin
Tudor Nicosevici
Armagan Elibol
Laszlo Neumann
Javier Escartin
7.1 Introduction
133(1)
7.2 Offline Computer Vision Techniques for Underwater Mapping and Inspection
134(23)
7.2.1 2D Mosaicing
134(10)
7.2.2 2.5D Mapping
144(2)
7.2.3 3D Mapping
146(8)
7.2.4 Machine Learning for Seafloor Classification
154(3)
7.3 Acoustic Mapping Techniques
157(2)
7.4 Concluding Remarks
159(2)
8 Closing Notes
161(3)
Antonio M. Lopez
References 164(31)
Index 195
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. 
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