Morphological Classification for Traffic Sign Recognition

In this paper, a novel method is proposed for the Traffic Sign Recognition (TSR) using the Principle Component Analysis (PCA) and the Multi-Layer Perceptron (MLPs) network. In particular to the proposed morphological classification method, the candidate signs are individually detected from two chrome components of the YCbCr space and then classified into three shape classes: circle, square, and triangle based on computing the rotated version correlations. The PCA-based features of these sign objects will be used for the MLPs as the training system corresponding to previously determined class. This approach not only reduces the time but also increases the performance in the recognition process. In simulation, the proposed method is estimated with over 500 statistic images and its accuracy rate is up to 96%.