Classifier comparison for repeating motion based video classification

Abstract

In this paper we introduce a repeating motion based video classification system. Videos from certain topical areas like sports, home improvement, or mechanical motion often show specific repeating movements. Main and side frequencies of these repetitions can be considered as motion features. We receive these features by the Fourier transform of spatio-temporal motion trajectories and use them during classification phase. Our experiments focus on various classifiers in order to find the most accurate classifier for motion frequency related features. © 2013 Springer-Verlag.