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In recent years, there has been renewed interest in developing methods forskeleton-based human action recognition. A skeleton sequence can be naturallyrepresented as a high-order tensor time series. In this paper, we model andanalyze tensor time series with Linear Dynamical System (LDS) which is the mostcommon for encoding spatio-temporal time-series data in various disciplines dutto its relative simplicity and efficiency. However, the traditional LDS treatsthe latent and observation state at each frame of video as a column vector.Such a vector representation fails to take into account the curse ofdimensionality as well as valuable structural information with human action.Considering this fact, we propose generalized Linear Dynamical System (gLDS)for modeling tensor observation in the time series and employ Tuckerdecomposition to estimate the LDS parameters as action descriptors. Therefore,an action can be represented as a subspace corresponding to a point on aGrassmann manifold. Then we perform classification using dictionary learningand sparse coding over Grassmann manifold. Experiments on MSR Action3D Dataset,UCF Kinect Dataset and Northwestern-UCLA Multiview Action3D Dataset demonstratethat our proposed method achieves superior performance to the state-of-the-artalgorithms.

Learning Linear Dynamical Systems with High-Order Tensor Data for Skeleton based Action Recognition