Human action recognition using similarity degree between postures and spectral learning

In recent years, there has been renewed interest in developing methods for skeleton-based human action recognition. In this study, the challenging problem of the similarity degree of skeleton-based human postures is addressed. Human posture is described by screw motions between 3D rigid bodies, which can be seen as a relation matrix of 3D rigid bodies (RMRB3D). A linear subspace, a point of a Grassmannian manifold, is spanned by the orthonormal basis of matrix RMRB3D. A powerful way to compute the similarity degree between postures is researched to solve the geodesic distance between points on the Grassmannian manifold. Then representative postures are extracted through spectral clustering over representative postures. An action will be represented by a symbol sequence generated with a global linear eigenfunction constructed by spectral embedding. Finally, dynamic time warping and hidden Markov model (HMM) are used to classify these action sequences. The experimental evaluations of the proposed method on several challenging 3D action datasets show that the proposed approaches achieve promising results compared with other skeleton-based human action recognition algorithms.