Multiclass classification based on clustering approaches for obstacle recognition in traffic scenes

Traffic scene object detection and recognition is extensively researched in the field of roadside assistance. Due to its importance, many methods have been proposed to solve the classification of objects in traffic and aim classification in different lighting conditions, scaling, orientation and shape of objects. Although most methods for classification are binary classification, often need multiclass classification to reduce the computational effort and especially for traffic are several items that need to be detected and classified. In this paper are tested several methods for multiclass classification.