This paper introduces a new method for streamed action recognition using Motion Capture (MoCap) data. First, the histograms of action poses, extracted from MoCap data, are computed according to Hausdorf distance. Then, using a dynamic programming algorithm and an incremental histogram computation, our proposed solution recognizes actions in real time from streams of poses. The comparison of histograms for recognition was achieved using Bhattacharyya distance. Furthermore, the learning phase has remained very efficient with respect to both time and complexity. We have shown the effectiveness of our solution by testing it on large datasets, obtained from animation databases. In particular, we were able to achieve excellent recognition rates that have outperformed the existing methods.
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