Action recognition using completed local binary patterns and multiple-class boosting classifier

This paper, for the first time, introduces a multiple-class boosting scheme (MBS) to combine depth motion maps (DMMs) and completed local binary patterns (CLBP) for action recognition. DMMs derive from projecting depth frames onto three orthogonal Cartesian planes (front, side and top) and characterize the motion energy of an action, on which the CLBP features are further extracted. And then a new multi-class boosting method is used and leads to an effective decision-level classifier. Extensive experiments on the MSRAction3D and MSRGesture3D datasets indicate that the proposed MBS method achieves new state-of-the-art results.