Recognition of traffic signs based on their colour and shape features extracted using human vision models
Article
Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. 2006. Recognition of traffic signs based on their colour and shape features extracted using human vision models. Journal of Visual Communication and Image Representation. 17 (4), pp. 675-685. https://doi.org/10.1016/j.jvcir.2005.10.003
Type | Article |
---|---|
Title | Recognition of traffic signs based on their colour and shape features extracted using human vision models |
Authors | Gao, X., Podladchikova, L., Shaposhnikov, D., Hong, K. and Shevtsova, N. |
Abstract | Colour and shape are basic characteristics of traffic signs which are used both by the driver and to develop artificial traffic sign recognition systems. However, these sign features have not been represented robustly in the earlier developed recognition systems, especially in disturbed viewing conditions. In this study, this information is represented by using a human vision colour appearance model and by further developing existing behaviour model of visions. Colour appearance model CIECAM97 has been applied to extract colour information and to segment and classify traffic signs. Whilst shape features are extracted by the development of FOSTS model, the extension of behaviour model of visions. Recognition rate is very high for signs under artificial transformations that imitate possible real world sign distortion (up to 50% for noise level, 50 m for distances to signs, and 5° for perspective disturbances) for still images. For British traffic signs (n = 98) obtained under various viewing conditions, the recognition rate is up to 95%. |
Keywords | transformation invariant recognition; traffic signs recognition; feature extraction |
Research Group | Artificial Intelligence group |
Publisher | Elsevier |
Journal | Journal of Visual Communication and Image Representation |
ISSN | 1047-3203 |
Electronic | 1095-9076 |
Publication dates | |
Aug 2006 | |
Publication process dates | |
Deposited | 15 Oct 2008 |
Output status | Published |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.jvcir.2005.10.003 |
Scopus EID | 2-s2.0-33646805441 |
Web of Science identifier | WOS:000242027500001 |
Language | English |
https://repository.mdx.ac.uk/item/80v21
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