Detecting Spatio-Temporally Interest Points Using the Shearlet Transform

Damiano Malafronte, Francesca Odone, Ernesto De Vito

Abstract: In this paper we address the problem of detecting spatio-temporal interest points in video sequences and we introduce a novel detection algorithm based on the three-dimensional shearlet transform. By evaluating our method on different application scenarios, we show we are able to extract meaningful spatio-temporal features from video sequences of human movements, including full body movements selected from benchmark datasets of human actions and human-machine interaction sequences where the goal is to segment drawing activities in smaller action primitives.