  In recent years, there has been renewed interest in developing methods for
skeleton-based human action recognition. A skeleton sequence can be naturally
represented as a high-order tensor time series. In this paper, we model and
analyze tensor time series with Linear Dynamical System (LDS) which is the most
common for encoding spatio-temporal time-series data in various disciplines dut
to its relative simplicity and efficiency. However, the traditional LDS treats
the latent and observation state at each frame of video as a column vector.
Such a vector representation fails to take into account the curse of
dimensionality 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 Tucker
decomposition to estimate the LDS parameters as action descriptors. Therefore,
an action can be represented as a subspace corresponding to a point on a
Grassmann manifold. Then we perform classification using dictionary learning
and sparse coding over Grassmann manifold. Experiments on MSR Action3D Dataset,
UCF Kinect Dataset and Northwestern-UCLA Multiview Action3D Dataset demonstrate
that our proposed method achieves superior performance to the state-of-the-art
algorithms.
