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Proceedings Paper

Robust traffic sign detection using fuzzy shape recognizer
Author(s): Lunbo Li; Jun Li; Jianhong Sun
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Paper Abstract

A novel fuzzy approach for the detection of traffic signs in natural environments is presented. More than 3000 road images were collected under different weather conditions by a digital camera, and used for testing this approach. Every RGB image was converted into HSV colour space, and segmented by the hue and saturation thresholds. A symmetrical detector was used to extract the local features of the regions of interest (ROI), and the shape of ROI was determined by a fuzzy shape recognizer which invoked a set of fuzzy rules. The experimental results show that the proposed algorithm is translation, rotation and scaling invariant, and gives reliable shape recognition in complex traffic scenes where clustering and partial occlusion normally occur.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74960Z (30 October 2009); doi: 10.1117/12.833428
Show Author Affiliations
Lunbo Li, Nanjing Univ. of Science and Technology (China)
Jun Li, Nanjing Univ. of Science and Technology (China)
Jianhong Sun, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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