• DocumentCode
    3287068
  • Title

    Geolocation on the iPhone by automatic street sign reading

  • Author

    Oosterman, Joshua ; Green, Richard

  • Author_Institution
    Dept. of Comput. Sci. & Software Eng., Univ. of Canterbury, Christchurch, New Zealand
  • fYear
    2010
  • fDate
    8-9 Nov. 2010
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In any country, roads are marked with signs. In particular, street signs are a type of road sign used to display the names of streets. Mobile devices such as smart phones are now powerful enough to serve as platforms for computer vision applications. These mobile devices are commonly equipped with high resolution cameras and increasingly with internal GPS for geolocation.We propose an accurate method of geolocation which detects, segments and reads street signs in complex natural scenes from an iPhone image. Street signs are detected using image segmentation based on edge detection and contour detection techniques. Sign candidates are selected using several heuristics, and then partitioned into individual characters. The letters are recognised using a nearest-neighbour algorithm for Optical Character Recognition (OCR). The street names finally are passed to a GeoCoding API to display a street map of the users location. We evaluate the system for a real word data set and achive 75% sign detection accuracy, 91% OCR accuracy and 55% total geolocation accuracy.
  • Keywords
    Global Positioning System; application program interfaces; edge detection; geography; image resolution; image segmentation; optical character recognition; smart phones; GeoCoding API; OCR; automatic street sign reading; computer vision application; contour detection technique; edge detection; geolocation; heuristics; high resolution cameras; iPhone; image segmentation; internal GPS; letter recognition; mobile devices; nearest-neighbour algorithm; optical character recognition; road sign; smart phones; street map; street signs detection; street signs reading; street signs segmentation; Accuracy; Algorithm design and analysis; Cameras; Geology; Image color analysis; Image edge detection; Optical character recognition software;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Vision Computing New Zealand (IVCNZ), 2010 25th International Conference of
  • Conference_Location
    Queenstown
  • ISSN
    2151-2191
  • Print_ISBN
    978-1-4244-9629-7
  • Type

    conf

  • DOI
    10.1109/IVCNZ.2010.6148877
  • Filename
    6148877