Vehicle rear detection in images with Generalized Radial-Basis-Function classifiers

Abstract

A classification system for vehicle rear detection in images is presented. The classification system consists of an expert system for preselecting relevant regions in an image and a subsequent machine learning classifier. Both utilize specifically designed features for the application. The latter classifier is implemented by a generalized radial basis function (GRBF) algorithm to assure the interpretability of the detection system. The GRBF is constructed based on the kernel of an ensemble method classifier, the random forest (RF) algorithm in order to achieve a low generalization error. The high accuracy of the proposed technique for vehicle rear detection is validated using data from real traffic situations.