In this paper, a human action recognition method is presented in which pose representation is based on the contour points of the human silhouette and actions are learned by making use of sequences of multi-view key poses. Our contribution is twofold. Firstly, our approach achieves state-of-the-art success rates without compromising the speed of the recognition process and therefore showing suitability for online recognition and real-time scenarios. Secondly, dissimilarities among different actors performing the same action are handled by taking into account variations in shape (shifting the test data to the known domain of key poses) and speed (considering inconsistent time scales in the classification). Experimental results on the publicly available Weizmann, MuHAVi and IXMAS datasets return high and stable success rates, achieving, to the best of our knowledge, the best rate so far on the MuHAVi Novel Actor test.
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