Cover Page

Computer Vision in Vehicle Technology

Land, Sea, and Air

 

Edited by

 

Antonio M. López

Computer Vision Center (CVC) and Universitat Autònoma de Barcelona, Spain

 

Atsushi Imiya

Chiba University, Japan

 

Tomas Pajdla

Czech Technical University, Czech Republic

 

Jose M. Álvarez

National Information Communications Technology Australia (NICTA), Canberra Research Laboratory, Australia

 

 

 

 

 

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List of Contributors

  1. Ricard Campos, Computer Vision and Robotics Institute, University of Girona, Spain
  2.  
  3. Arturo de la Escalera, Laboratorio de Sistemas Inteligentes, Universidad Carlos III de Madrid, Spain
  4.  
  5. Armagan Elibol, Department of Mathematical Engineering, Yildiz Technical University, Istanbul, Turkey
  6.  
  7. Javier Escartin, Institute of Physics of Paris Globe, The National Centre for Scientific Research, Paris, France
  8.  
  9. Uwe Franke, Image Understanding Group, Daimler AG, Sindelfingen, Germany
  10.  
  11. Friedrich Fraundorfer, Institute for Computer Graphics and Vision, Graz University of Technology, Austria
  12.  
  13. Rafael Garcia, Computer Vision and Robotics Institute, University of Girona, Spain
  14.  
  15. David Gerónimo, ADAS Group, Computer Vision Center, Universitat Autònoma de Barcelona, Spain
  16.  
  17. Nuno Gracias, Computer Vision and Robotics Institute, University of Girona, Spain
  18.  
  19. Ramon Hegedus, Max Planck Institute for Informatics, Saarbruecken, Germany
  20.  
  21. Natalia Hurtos, Computer Vision and Robotics Institute, University of Girona, Spain
  22.  
  23. Reinhard Klette, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, New Zealand
  24.  
  25. Antonio M. López, ADAS Group, Computer Vision Center (CVC) and Computer Science Department, Universitat Autònoma de Barcelona (UAB), Spain
  26.  
  27. Laszlo Neumann, Computer Vision and Robotics Institute, University of Girona, Spain
  28.  
  29. Tudor Nicosevici, Computer Vision and Robotics Institute, University of Girona, Spain
  30.  
  31. Ricard Prados, Computer Vision and Robotics Institute, University of Girona, Spain
  32.  
  33. Davide Scaramuzza, Robotics and Perception Group, University of Zurich, Switzerland
  34.  
  35. ASM Shihavuddin, École Normale Supérieure, Paris, France
  36.  
  37. David Vázquez, ADAS Group, Computer Vision Center, Universitat Autònoma de Barcelona, Spain
  38.  

Preface

This book was born following the spirit of the Computer Vision in Vehicular Technology (CVVT) Workshop. At the moment of finishing this book, the 7th CVVT Workshop CVPR'2016 is being held in Las Vegas. Previous CVVT Workshops include the CVPR'2015 in Boston (http://adas.cvc.uab.es/CVVT2015/), ECCV'2014 in Zurich (http://adas.cvc.uab.es/CVVT2014/), ICCV'2013 in Sydney (http://adas.cvc.uab.es/CVVT2013/), ECCV'2012 in Firenze (http://adas.cvc.uab.es/CVVT2012/), ICCV'2011 in Barcelona (http://adas.cvc.uab.es/CVVT2011/), and ACCV'2010 in Queenstown (http://www.media.imit.chiba-u.jp/CVVT2010/). This implies throughout these years, many invited speakers, co-organizers, contributing authors, and sponsors have helped to keep CVVT alive and exciting. We are enormously grateful to all of them! Of course, we also want to give special thanks to the authors of this book, who kindly accepted the challenge of writing their respective chapters.

He would also like to thank the past and current members of the Advanced Driver Assistance Systems (ADAS) group of the Computer Vision Center at the Universitat Autònoma de Barcelona. He also would like to thank his current public funding, in particular, Spanish MEC project TRA2014-57088-C2-1-R, Spanish DGT project SPIP2014-01352, and the Generalitat de Catalunya project 2014-SGR-1506. Finally, he would like to thank NVIDIA Corporation for the generous donations of different graphical processing hardware units, and especially for their kind support regarding the ADAS group activities.

Tomas Pajdla has been supported by EU H2020 Grant No. 688652 UP-Drive and Institutional Resources for Research of the Czech Technical University in Prague.

Atsushi Imiya was supported by IMIT Project Pattern Recognition for Large Data Sets from 2010 to 2015 at Chiba University, Japan.

Jose M. Álvarez was supported by the Australian Research Council through its Special Research Initiative in Bionic Vision Science and Technology grant to Bionic Vision Australia. The National Information Communications Technology Australia was founded by the Australian Government through the Department of Communications and the Australian Research Council through the ICT Center of Excellence Program.

The book is organized into seven self-contained chapters related to CVVT topics, and a final short chapter with the overall final remarks. Briefly, in Chapter 1, there is a quick overview of the main ideas that link computer vision with vehicles. Chapters 2–7 are more specialized and divided into two blocks. Chapters 2–4 focus on the use of computer vision for the self-navigation of the vehicles. In particular, Chapter 2 focuses on land (autonomous cars), Chapter 3 focuses on air (micro aerial vehicles), and Chapter 4 focuses on sea (underwater robotics). Analogously, Chapters 5–7 focus on the use of computer vision as a technology to solve specific applications beyond self-navigation. In particular, Chapter 5 focuses on land (ADAS), and Chapters 6 and 7 on air and sea, respectively. Finally, Chapter 8 concludes and points out new research trends.

Antonio M. López

Computer Vision Center (CVC) and
Universitat Autònoma de Barcelona, Spain

Abbreviations and Acronyms

ACCadaptive cruise control
ADASadvanced driver assistance system
AUVautonomous underwater vehicle
BAbundle adjustment
BCMbrightness constancy model
BoWbag of words
CANcontroller area network
CLAHEcontrast limited adaptive histogram equalization
COTScrown of thorns starfish
DCTdiscrete cosine transforms
DOFdegree of freedom
DVLDoppler velocity log
EKFextended Kalman filter
ESCelectronic stability control
FCAforward collision avoidance
FEMfinite element method
FFTfast Fourier transform
FIRfar infrared
FLSforward-looking sonar
GAglobal alignment
GDIMgeneralized dynamic image model
GLCMgray level co-occurrence matrix
GPSglobal positioning system
GPUgraphical processing unit
HDRhigh dynamic range
HOGhistogram of gradients
HOVhuman operated vehicle
HSVhue saturation value
IRinfrared
KPCAkernel principal component analysis
LBLlong baseline
LBPlocal binary patterns
LCAlane change assistance
LDAlinear discriminant analysis
LDWlane departure warning
LHClocal homogeneity coefficient
LKSlane keeping system
LMedSleast median of squares
MEXMATLAB executable
MLSmoving least squares
MRmaximum response
MSTminimum spanning tree
NCCnormalized chromaticity coordinates
NDTnormal distribution transform
NIRnear infrared
OVVonline visual vocabularies
PCAprincipal component analysis
PDWMDprobability density weighted mean distance
PNNprobabilistic neural network
RANSACrandom sample consensus
RBFradial basis function
RODregion of difference
ROIregion of interest
ROVremotely operated vehicle
SDFsigned distance function
SEFseam-eliminating function
SIFTscale invariant feature transform
SLAMsimultaneous localization and mapping
SNRsignal-to-noise ratio
SSDsum of squared differences
SURFspeeded up robust features
SVMsupport vector machine
TJAtraffic jam assist
TSRtraffic sign recognition
TVtotal variation
UDFunsigned distance function
USBLultra short base line
UUVunmanned underwater vehicle
UVunderwater vehicle