Abstract—This paper proposes an efficient real time driving
sign “stop” detection method using template matching, genetic
algorithms and neural network. Stop signs are usually installed
on junctions without traffic lights to warn drivers. However,
many accidents still occur at these locations because either the
driver does not notice the signs or just ignore them. Therefore,
to reduce such accidents, we propose an automatic stop sign
detection method to aid the drivers and also contribute to future
automatic driving system such as the Intelligent Traffic System
(ITS). Our method efficiently extracts the sign region´s
candidate regions, performs template matching using genetic
algorithms and verifies the presence of the road sign using
neural networks. Although, we face various problems including
camera shake and complicated scenes, our method produces an
encouraging accuracy of about 96%.
Index Terms—Genetic algorithms, road signs, template
matching, neural networks.
The authors are with the Graduate School of Advanced Technology and
Science, The University of Tokushima (e-mail:
karunga@is.tokushima-u.ac.jp).
Cite:Stephen Karungaru, Hitoshi Nakano, and Minoru Fukumi, "Road Traffic Signs Recognition Using Genetic Algorithms and Neural Networks," International Journal of Machine Learning and Computing vol.3, no. 3, pp. 313-317, 2013.