Skeleton-Based Human Action Recognition via Screw Matrices

With the recent advent of low-cost acqui-sition depth cameras, extracting 3D body skeleton has be-come relatively easier, which significantly lighten many dif-ficulties in 2D videos including occlusions, shadows and background extraction, etc. Directly perceived features, for example points, lines and planes, can be easily ex-tracted from 3D videos such that we can employ rigid motions to represent skeletal motions in a geometric way. We apply screw matrices, acquired by using rotations and translations in 3D space, to model single and multi-body rigid motion. Since screw matrices are members of the special Euclidean group SE(3), an action can be repre-sented as a point on a Lie group, which is a differen-tiable manifold. Using Lie-algebraic properties of screw al-gebra, isomorphic to se(3), the classical algorithms of ma-chine learning in vector space can be expanded to man-ifold space. We evaluate our approached on three public 3D action datasets: MSR Action3D dataset, UCF Kinect dataset and Florence3D-Action Dataset. The experimental results show that our approaches either match or exceed state-of-the-art skeleton-based human action recognition approaches.