A New Method For Mapping Active Joint Locations Of Skeletons To Pre-Shape Space For Action Recognition

Being a class of effective feature descriptors for action recognition, action representations based on skeleton sequences have yielded excellent recognition results. Most methods used to construct these action representations are based on the information from all the joint positions in actions. Unfortunately, some joints in the actions do not improve the accuracy of action recognition, and may even cause unnecessary inter-class errors. In this study, the authors propose a new method for action recognition by selecting active joints which are closely related to the movement of the body as the first step. Further, a skeleton is characterized as a set of its active-joint positions, and the set can be mapped to a point on pre-shape space to filter out the scale and translation variability. Then, a skeleton sequence (an action) can be regarded as points on the space. Because the timing-sequence relationship between skeletons is very valuable for action recognition, a tensor-based linear dynamical system (tLDS) is employed to model the temporal information of the action. To avoid using a finite-order sequence to estimate the infinite-order feature descriptor of a tLDS, the descriptor is mapped to a point on an infinite Grassmannian composed of the extended observability subspaces. The action is classified using sparse coding and dictionary learning (SCDL) on the infinite Grassmannian. Experimental results demonstrate that the recognition accuracies of the proposed method outperform state-of-the-art ones on four different action datasets.