Abstract
In this paper, we propose a new framework for view independent action recognition, which uses a combination of a view-dependent representation and a view-independent representation. The view-dependent representation reduces the number of possible action's labels prior to the view-independent representation. We used the entropy of silhouette's distance transformation as view-dependent representation and the self-similarity matrix of the trajectory of uniformly distributed feature points over the human body as view-independent representation. The experiment results show that the proposed method outperforms recent action recognition approaches despite its low computational cost.
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