Wide-angle camera technology for automotive applications: a review

Wide-angle camera technology for automotive applications: a review

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The development of electronic vision systems for the automotive market is a strongly growing field, driven in particular by customer demand to increase the safety of vehicles both for drivers and for other road users, including vulnerable road users (VRUs), such as pedestrians. Customer demand is matched by legislative developments in a number of key automotive markets; for example Europe, Japan and the US are in the process of introducing legislation to aid in the prevention of fatalities to VRUs, with emphasis on the use of vision systems.The authors discuss some of the factors that motivate the use of wide-angle and fish-eye camera technologies in vehicles. The authors describe the benefits of using wide-angle lens camera systems to display areas of a vehicle's surroundings that the driver would, otherwise, be unaware of (i.e. a vehicle's blind-zones). However, although wide-angle optics provide greater fields of view, they also introduce undesirable effects, such as radial distortion, tangential distortion and uneven illumination.These distortions have the potential to make objects difficult for the vehicle driver to recognise and, thus, potentially have a greater risk of accident. The authors describe some of the methods that can be employed to remove these unwanted effects, and digitally convert the distorted image to the ideal and intuitive rectilinear pin-hole model.


    1. 1)
      • European New Car Assessment Program (Euro NCAP):, accessed March 2008.
    2. 2)
      • Hobbs, A.: `Euro NCAP/MORI survey on consumer buying interests (speech and presentation)', Proc. Euro NCAP conf.: creating a market for safety – 10 years of Euro NCAP, November 2005, Brussels, Belgium.
    3. 3)
      • Japanese National Agency for Automotive Safety and Victims Aid: ‘New Car Assessment (JNCAP)’., accessed March 2008.
    4. 4)
      • US National Highway Traffic Safety Administration: ‘USNCAP’., accessed March 2008.
    5. 5)
      • Department for Transport Research Database: ‘Secondary safety’., accessed June 2008.
    6. 6)
      • Department for Transport Research Database: ‘Primary and e-safety’., accessed June 2008.
    7. 7)
      • UNECE Working Party on General Safety Provisions (GRSG): ‘GRSG-83-Inf15e: Outline of Draft Amendment to ECE Regulation No.46 (Draft Requirements for Driver's Field of Vision of Immediate Frontward and Sideward)’, 2002.
    8. 8)
      • Consumers Union: ‘Consumer reports – blind-zone measurements’., accessed March 2008.
    9. 9)
      • Ehlgen, T., Paidla, T.: `Maneuvering aid for large vehicle using omnidirectional cameras', Proc. IEEE Workshop on Applications of Computer Vision, February 2007, Austin, Texas, US, p. 17–17.
    10. 10)
      • European Commission: ‘Community database on accidents on the roads in Europe (CARE)’., accessed November 2007.
    11. 11)
      • European Commission Directorate-General for Energy and Transport: ‘Halving the number of road accident victims in the EU by 2010: a shared responsibility’ (European Road Safety Action Programme), 2004.
    12. 12)
      • Commission of the European Communities: ‘Commission staff working document: accompanying document to the proposal for a directive of the European Parliament and of the Council on the retrofitting of mirrors to heavy goods vehicles 19 registered in the Community Full Impact Assessment COM(2006)570’, October 2006.
    13. 13)
      • UK Health and Safety Executive: ‘Statistics: number of workplace transport injuries’., accessed March 2008.
    14. 14)
      • Kids and Cars Organisation:, accessed March 2008.
    15. 15)
      • E. McLoughlin , J. Middlebrooks , J. Annest , P. Holmgreen , A. Dellinger . Injuries and deaths among children left unattended in or around motor vehicles – US, July 2000–June 2001. Morb. Mortal. Wkly. Rep. , 26 , 570 - 572
    16. 16)
      • R. Patel , A. Dellinger , J. Annest . Nonfatal motor-vehicle–related backover injuries among children – United States, 2001–2003. Morb. Mortal. Wkly. Rep. , 6 , 144 - 146
    17. 17)
      • Wang, J., Knipling, R.: `Lane change/merge crashes: problem size assessment and statistical description', Report No. DOT HS 808-075, Technical report, 1994, United States Department of Transportation Publication.
    18. 18)
      • European Parliament and Council: ‘Directive 2003/97/EC of 10 November 2003 on the approximation of the laws of the Member States relating to the typeapproval of devices for indirect vision and of vehicles equipped with these devices, amending Directive 70/156/EEC and repealing Directive 71/127/EEC’. November 2003.
    19. 19)
      • European Parliament and Council: ‘Directive 2007/38/EC of 11 July 2007 on the retrofitting of mirrors to heavy goods vehicles registered in the Community’. July 2007.
    20. 20)
      • UNECE Working Party on General Safety Provisions (GRSG): ‘GRSG-89-26: Proposal for Step-2 revision of Regulation No. 46’. 2005.
    21. 21)
      • American Library of Congress: ‘S.694: Cameron Gulbransen Kids and Cars Safety Act of 2007 (Reported in Senate)’., accessed June 2008.
    22. 22)
      • Brauer-Burchardt, C., Voss, K.: `A new algorithm to correct fish-eye- and strong wide-angle-lens-distortion from single images', Proc. IEEE Int. Conf. Image Processing, October 2001, Thessaloniki, Greece, 1, p. 225–228.
    23. 23)
      • Barreto, J.P., Daniilidis, K.: `Fundamental matrix for cameras with radial distortion', Proc. IEEE Int. Conf. Computer Vision, October 2005, Beijing, China, 1, p. 625–632.
    24. 24)
      • Strand, R., Hayman, E.: `Correcting radial distortion by circle fitting', Proc. BMVA British Machine Vision Conf., September 2005, Oxford, UK.
    25. 25)
      • C. McGlone , E. Mikhail , J. Bethel . (2004) Manual of photogrammetry.
    26. 26)
      • R.Y. Tsai . A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J. Robot. Autom. , 4 , 323 - 344
    27. 27)
      • J. Weng , P. Cohen , M. Herniou . Camera calibration with distortion models and accuracy evaluation. IEEE Trans. Pattern Anal. Mach. Intelli. , 10 , 965 - 980
    28. 28)
    29. 29)
      • Devernay, F., Faugeras, O.D.: `Automatic calibration and removal of distortion from scenes of structured environments', Proc. SPIE Investigative and Trial Image Processing Conf., 1995, San Diego, California, US, 2567, p. 62–72.
    30. 30)
    31. 31)
      • Fitzgibbon, A.W.: `Simultaneous linear estimation of multiple view geometry and lens distortion', Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, December 2001, Kauai, Hawaii, US, 1, p. 125–132.
    32. 32)
      • Mallon, J., Whelan, P.F.: `Precise radial un-distortion of images', Proc. IEEE Int. Conf. Pattern Recognition, August 2004, Surrey, UK, 1, p. 18–21.
    33. 33)
      • K.V. Asari . Design of an efficient VLSI architecture for non-linear spatial warping of wide-angle camera images. J. Sys. Architec. , 12 , 743 - 755
    34. 34)
    35. 35)
      • Thirthala, S., Pollefeys, M.: `The radial trifocal tensor: a tool for calibrating the radial distortion of wide-angle cameras', Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, June 2005, San Diego, California, US, 1, p. 321–328.
    36. 36)
      • S. Shah , J.K. Aggarwal . Intrinsic parameter calibration procedure for a (high-distortion) fish-eye lens camera with distortion model and accuracy estimation. J. Pattern Recognit. , 11 , 1775 - 1788
    37. 37)
      • Basu, A., Licardie, S.: `Modeling fish-eye lenses', Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, July 1993, Yokohama, Japan, 3, p. 1822–1828.
    38. 38)
      • Shah, S., Aggarwal, J.K.: `A simple calibration procedure for fish-eye (high distortion) lens camera', Proc. IEEE Int. Conf. Robotics and Automation, April 1994, New Orleans, Louisiana, US, 4, p. 3422–3427.
    39. 39)
      • Ishii, C., Sudo, Y., Hashimoto, H.: `An image conversion algorithm from fish eye image to perspective image for human eyes', Proc. IEEE/ASME Int. Conf. Advanced Intelligent Mechatronics, July 2003, Port Island, Kobe, Japan, 2, p. 1009–1014.
    40. 40)
      • F. Devernay , O. Faugeras . Straight lines have to be straight: automatic calibration and removal of distortion from scenes of structured enviroments. Int. J. Mach. Vis. Appl. , 1 , 14 - 24
    41. 41)
      • Bogner, S.L.: `An introduction to panospheric imaging', Proc. IEEE Int. Conf. Systems, Man and Cybernetics Intelligent Systems for the 21st Century, October 1995, Vancouver, British Columbia, Canada, 4, p. 3099–3106.
    42. 42)
      • Fernandes, J.C.A., Ferreira, M.J.O., Neves, J.A.B.C., Couto, C.A.C.: `Fast correction of lens distortion for image applications', Proc. IEEE Int. Symp. Industrial Electronics, July 1997, Guimar̃aes, Portugal, 2, p. 708–712.
    43. 43)
      • Bakstein, H., Pajdla, T.: `Panoramic mosaicing with a 180degree field of view lens', Proc. IEEE Workshop on Omnidirectional Vision, June 2002, Hilton Head Island, South Carolina, US, p. 60–67.
    44. 44)
      • Zhang, Z.: `Flexible camera calibration by viewing a plane from unknown orientations', Proc. IEEE Int. Conf. Computer Vision, September 1999, Kerkyra, Greece, 1, p. 666–673.
    45. 45)
      • Brauer-Burchardt, C., Voss, K.: `Automatic lens distortion calibration using single views', Mustererkennung, DAGM-Symposium, September 2000, Kiel, Germany, p. 187–194.
    46. 46)
      • Stein, G.P.: `Lens distortion calibration using point correspondences', Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition, June 1997, San Juan, Puerto Rico, p. 602–608.
    47. 47)
      • Yu, W., Chung, Y.: `An embedded camera lens distortion correction method for mobile computing applications', Proc. IEEE Int. Conf. Consumer Electronics, June 2003, Los Angeles, California, US, p. 400–401.
    48. 48)
      • Ruiz, A., Lopez-de Teruel, P.E., Garcia-Mateos, G.: `A note on principal point estimability', Proc. IEEE Int. Conf. Pattern Recognition, August 2002, Quebec, Canada, 2, p. 304–307.
    49. 49)
      • Stein, G.P.: `Internal camera calibration using rotation and geometric shapes', 1993, M.Sc, Massachusetts Institute of Technology.
    50. 50)
    51. 51)
      • W.T. Welford . (1991) Useful optics (Chicago lectures in physics).
    52. 52)
      • Aggarwal, M., Hua, H., Ahuja, N.: `On cosine-fourth and vignetting effects in real lenses', Proc. IEEE Int. Conf. Computer Vision, July 2001, Vancouver, British Columbia, Canada, 1, p. 472–479.
    53. 53)
      • Asada, N., Amano, A., Baba, M.: `Photometric Calibration of Zoom Lens Systems', Proc. IEEE Int. Conf. Pattern Recognition, August 1996, Vienna, Austria, 1, p. 186–190.
    54. 54)
      • Yu, W., Chung, Y., Soh, J.: `Vignetting distortion correction method for high quality digital imaging', Proc. IEEE Int. Conf. Pattern Recognition, August 2004, Surrey, UK, 3, p. 666–669.
    55. 55)
      • F.J.W.M. Leong , M. Brady , J.O. McGee . Correction of uneven illumination (vignetting) in digital microscopy images. BMJ J. Clinical Pathology , 619 - 621
    56. 56)
      • G. Wyszecki , W.S. Stiles . (1982) Color science: concepts and methods, quantitative data and formulae.

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