A hierarchical action recognition system applying fisher discrimination dictionary learning via sparse representation

Authors:
Ruihan Bao;Tadashi Shibata
Affiliations:
Department of Electrical Engineering and Information System, The University of Tokyo, Tokyo, Japan;Department of Electrical Engineering and Information System, The University of Tokyo, Tokyo, Japan
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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Abstract

In this paper, we propose a hierarchical action recognition system applying Fisher discrimination dictionary learning via sparse representation classifier. Feature vectors used to represent certain actions are first generated by employing local features extracted from motion field maps. Sparse representation classification (SRC) are then employed on those feature vectors, in which a structured dictionary for classification is learned applying Fisher discrimination dictionary learning (FDDL). We tested our algorithms on Weizmann human database and KTH human database, and compared the recognition rates with other modeling methods such as k-nearest neighbor. Results showed that the action recognition system applying FDDL can achieve better performance despite that the learning stage for the Fisher discrimination dictionary can converge within only several iterations.