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Action Recognition Method Based on Sets of Time Warped ARMA Models

In this paper, we propose a novel method for recognizing human actions from sequential body skeleton data. Our method is based on ARMA (Autoregressive Mean Average) model, which is constructed from the matrix of 3D joint positions time-series. The intrinsic structure of an action can be compactly summarized by the observability matrix of the ARMA model. Since the column vectors of an observability matrix span a subspace, given two ARMA models, we can measure the similarity between them by the canonical angles between the corresponding subspaces. This framework based on subspace representation is useful for action recognition. However, it does not work well when handling various actions with different action speeds, since optimal row size of each observability matrix depends on the action speed. To address this limitation, we perform a random sampling operation to the row elements in each observability matrix, while preserving the order of the elements. By repeating this operation, we generate a set of various time-warped ARMA models with various local motion speeds. The essence of this idea is that a whole set of such time-warped ARMA models is invariant to the changes in action speed. Furthermore, to construct an effective classification framework, we applied Grassmann discriminant analysis to the time-warped ARMA models. The effectiveness of the proposed method is demonstrated through comparison experiments with state-of-the-art methods on two public datasets: MSR 3D action dataset and UT-Kinect dataset.