Leveraging Skeleton Structure and Time Dependencies in the Scope of Action Recognition

Abstract

In this work, the structure of the moving skeleton, which is a time varying graph, along with the temporal dependencies of human action were leveraged in the scope of skeleton action recognition. The optimization of the proposed model shares similarities with the optimization problem of Slow Feature Analysis (SFA) enabling a well defined solution. Moreover, due to the incorporated skeleton structure, the learned slow functions enclose information regarding the geometry of the skeleton movement which is very useful in the action recognition problem. Two skeleton action datasets were used to evaluate our method, the MSR Action 3D and a dataset whose actions were inspired by psychological studies. Both datasets were captured by depth cameras. The proposed method yielded promising results when evaluated on the aforementioned datasets.