Symmetry enhanced Adaboost

Abstract

This paper describes a method to minimize the immense training time of the conventional Adaboost learning algorithm in object detection by reducing the sampling area. A new algorithm with respect to the geometric and accordingly the symmetric relations of the analyzed object is presented. Symmetry enhanced Adaboost (SEAdaboost) can limit the scanning area enormously, depending on the degree of the objects symmetry, while it maintains the detection rate. SEAdaboost allows to take advantage of the symmetric characteristics of an object by concentrating on corresponding symmetry features during the detection of weak classifiers. In our experiments we gain 39% reduced training time (in average) with slightly increasing detection rates (up to 2.4% and up to 6% depending on the object class) compared to the conventional Adaboost algorithm. © 2010 Springer-Verlag.