3D human action recognition based on the Spatial-Temporal Moving Skeleton Descriptor
Hongxian Yao, Xinghao Jiang, Tanfeng Sun, Shilin Wang · 2017
With the popularization of the Kinect sensor, human actions can be recognized based on the 3D skeletal information. In this paper, the Spatial-Temporal Moving Skeleton Descriptor (STMSD) is proposed by the fusion of three complementary features which are the Relative Geometric Velocity (RGV) between body parts, Relative Joint Positions (RJP), and Joint Angles (JA). The STMSD descriptor gives a complete view of the body skeleton in space and time. Among the three features, the Relative Geometric Velocity (RGV) is first proposed in our work. Inspired by the relative geometry using the Lie group and the Lie algebra, RGV describes the variation rates of body transformations which include 3D rotations and translations. Then interpolation and normalization are applied in frame descriptors. After the temporal modeling, Principal Component Analysis (PCA) is utilized. Experimental results on three datasets show that our approach performs better than existing action recognition approaches, including skeleton-based and other types.