Ensemble of ANN for Traffic Sign Recognition

Abstract

“Machine Learning (ML) is the subfield of Artificial Intelligence conceived with the bold objective to develop computational methods that would implement various forms of learning, in particular mechanisms capable of inducing knowledge form examples or data” (Kubat, Bratko & Michalski, 1998, p. 3). The simplest and best-understood ML task is known as supervised learning. In supervised learning, each example consists of a vector of features (x) and a class (y). The goal of the learning algorithm is, given a set of examples and their classes, find a function, f, that can be applied to assign the correct class to new examples. When the function f takes values from a discrete set of classes {C1, .…., CK,}, f is called a classifier (Dietterich, 2002). In the last decades it has been proved that learning tasks in which the unknown function f takes more than two values (multi-class learning problems) the better approach is to decompose the problem into multiple two-class classification problems (Ou & Murphey, 2007) (Dietterich, & Bakiri, 1995) (Massulli & Valentini, 2000). This article describes the implementation of a system whose main task is to classify prohibition road signs into several categories. In order to reduce the learning problem complexity and to improve the classification performance, the system is composed by a collection (ensemble) of independent binary classifiers. In the proposed approach, each binary classifier is a singleoutput neural network (NN) trained to distinguish a particular road sign kind from the others. The proposed system is a part of a Driver Support System (DSS) supported by the Spanish Government under project TRA2004-07441-C03-C02. For this reason, one of the main system requirements is that it should be implemented in hardware in order to use it aboard a vehicle for real time categorization. In order to fulfill this constraint, a reduction in the number of features that describe the instances must be performed. As consequence if we have k generic road sign types we will use k binary NN and k feature selection process will be executed.