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.
CITATION STYLE
Malafronte, D., Odone, F., & De Vito, E. (2017). Detecting spatio-temporally interest points using the shearlet transform. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10255 LNCS, pp. 501–510). Springer Verlag. https://doi.org/10.1007/978-3-319-58838-4_55
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