Traffic sign classification with batch and on-line linear support vector machines

This paper presents a comprehensive benchmark of several feature types and colorspace representations on the task of traffic sign classification. We focus on linear Support Vector Machine classifiers, and test several multi-class formulations, as well as a formulation that allows on-line training and updates. Experiments on two standard traffic sign classification datasets show that despite their relative simplicity, these classifiers offer competitive performance, and ultimately allow design of a flexible classification system in the context of application for automatic maintenance of traffic signalization inventory.