Recognition of Road Signs with Mixture of Neural Networks and Arbitration Modules
The automatic detection and recognition of road signs play important role in the driver assistance systems and can increase the safety on the roads. In this paper we propose a system of a road signs classifier which is based on ensemble of the non Euclidean distance neural networks and an arbitration unit. The input to this system comes from the sign detection module which supplies a normalized, binarized and resampled pictogram of a detected sign. The system performs classification on deformable models. The classifier is composed of a mixture of experts (binary distance neural networks) operating on slightly tilted or shifted versions of pictograms. This ensemble of experts is orchestrated by an arbitration module which operates in the winner-takes-all mode with a novel modification of promoting the most populated group of unanimous experts. The experimental results showed great robustness of the system and very fast response time which is an important factor in the driving assistance systems.
KeywordsInput Pattern Deformable Model Road Sign Driver Assistance System Real Scene
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