On-line boosting is a recent advancement in the field of machine learning that has opened a new spectrum of possibilities in many diverse fields. With respect to a static strong classifier, the on-line algorithm updates the ensemble using new incoming samples. This idea has been successfully exploited in tasks such as detection and tracking as a classification problem with good results. Our purpose is to provide an efficient and robust framework to build a cascade of on-line updated classifiers that, speeding up the application time, allows the employment of a higher number of features, thus achieving better detection performance.
