Colour Vision Model-Based Approach for Segmentation of Traffic Signs

This paper presents a new approach to segment traffic signs from the rest of a scene via CIECAM, a colour appearance model. This approach not only takes CIECAM into practical application for the first time since it was standardised in 1998, but also introduces a new way of segmenting traffic signs in order to improve the accuracy of colour-based approach. Comparison with the other CIE spaces, including CIELUV and CIELAB, and RGB colour space is also carried out. The results show that CIECAM performs better than the other three spaces with 94%, 90%, and 85% accurate rates for sunny, cloudy, and rainy days, respectively. The results also confirm that CIECAM does predict the colour appearance similar to average observers.

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Affiliations

1. School of Computing Science, Middlesex University, The Burroughs, Hendon, London, NW4 4BT, UK

   • Xiaohong Gao
   • , Kunbin Hong
   •  & Peter Passmore

2. Laboratory of Neuroinformatics of Sensory and Motor Systems, A.B. Kogan Research Institute for Neurocybernetics, Rostov State University, Rostov-on-Don, 344090, Russia

   • Lubov Podladchikova
   •  & Dmitry Shaposhnikov

Corresponding author

Correspondence to Xiaohong Gao.

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