Multi-class Boosting with Color-Based Haar-Like Features

Abstract

This paper presents a multi-class boosting algorithm employing color-based Haar-like features. Traditional multi-class boosting algorithms basically regard multi-class problems as extensions of two-class problems. In particular, additional strong classifiers must be parallelly extended once the number of target classes increases. The idea in the proposed approach is to develop a single strong classifier which is capable of resolving multi-class problems. To make the multi-class algorithm tractable, the proposed system is required to select a set of weak classifiers which could classify multiple types of targets correctly. In contrast to standard Haar-like features that compute feature values based on gray level images, the seemingly novel Haar-like features require computation based on color images. Since the mapping from color image space to gray level image space is an epimorphism, detection algorithms using standard Haar-like features inevitably disregard color information available in original color images. Strong classifiers adopting the proposed color-based Haar-like features typically appear to have comparable performance, in the aspects of detection and correct classification rates, with fewer weak classifiers when compared with the one employing standard Haar-like features. The proposed boosting algorithm can improve system efficiency and resolve multi-class problems by a single strong classifier, whereas existing approaches are more complicated and the number of two-class classifiers could be relatively large. Our approach has been successfully validated in real traffic environments by performing experiments with a CCD camera mounted onboard a highway vehicle, where the targets are defined as passenger cars and motorcycles.