VISION MODELS BASED IDENTIFICATION OF TRAFFIC SIGNS

Gao X.1,Shevtsova N.2, Hong K.1, Batty S.1, Podladchikova L.2, Golovan A.2, Shaposhnikov D.2, Gusakova V.2

1School of Computing Science, Middlesex University
London, United Kingdom
2A.B. Kogan Research Institute for Neurocybernetics,
Rostov State University, Rostov-on-Don, Russia


Abstract

During the last 10 years, computer hardware technology has been improved rapidly. Large memory, storage is no longer a problem. Therefore some trade-off (dirty and quick algorithms) for traffic sign recognition between accuracy and speed should be improved. In this study, a new approach has been developed for accurate and fast recognition of traffic signs based on human vision models. It applies colour appearance model CIECAM97s to segment traffic signs from the rest of scenes. A Behavioural Model of Vision (BMV) is then utilised to identify the signs after segmented images are converted into grey-level representation. Two standard traffic sign databases are established. One is British traffic signs and the other is Russian traffic signs. Preliminary results show that around 90% signs taken from the British road with various viewing conditions have been correctly identified.

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