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
Link To Document :
بازگشت