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HMMs are widely used in action and gesture recognition due to theirimplementation simplicity, low computational requirement, scalability and highparallelism. They have worth performance even with a limited training set. Allthese characteristics are hard to find together in other even more accuratemethods. In this paper, we propose a novel double-stage classificationapproach, based on Multiple Stream Discrete Hidden Markov Models (MSD-HMM) and3D skeleton joint data, able to reach high performances maintaining alladvantages listed above. The approach allows both to quickly classifypre-segmented gestures (offline classification), and to perform temporalsegmentation on streams of gestures (online classification) faster than realtime. We test our system on three public datasets, MSRAction3D, UTKinect-Actionand MSRDailyAction, and on a new dataset, Kinteract Dataset, explicitly createdfor Human Computer Interaction (HCI). We obtain state of the art performanceson all of them.

Fast Gesture Recognition with Multiple Stream Discrete HMMs on 3D Skeletons