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|Title: ||Road traffic sign detection and classification|
|Author(s): ||Escalera, Arturo de la|
Salichs, Miguel A.
Armingol, José M.
|Issued date: ||1997|
|Citation: ||IEEE Transactions on Industrial Electronics, 1997, vol. 44, n. 6, p. 848-859|
|Abstract: ||A vision-based vehicle guidance system for road vehicles can have three main roles: (1) road detection; (2) obstacle detection; and (3) sign recognition. The first two have been studied for many years and with many good results, but traffic sign recognition is a less-studied field. Traffic signs provide drivers with very valuable information about the road, in order to make driving safer and easier. The authors think that traffic signs most play the same role for autonomous vehicles. They are designed to be easily recognized by human drivers mainly because their color and shapes are very different from natural environments. The algorithm described in this paper takes advantage of these features. It has two main parts. The first one, for the detection, uses color thresholding to segment the image and shape analysis to detect the signs. The second one, for the classification, uses a neural network. Some results from natural scenes are shown.|
|Publisher version: ||http://dx.doi.org/10.1109/41.649946|
|Keywords: ||Computer vision|
Driver information systems
|Rights: ||© IEEE|
|Appears in Collections:||DISA - LSI - Artículos de Revistas|
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