Spatial Joint features for 3D human skeletal action recognition system using spatial graph kernels

Abstract

Human action recognition is a vibrant area of research with multiple application areas in human machine interface. In this work, we pro-pose a human action recognition based on spatial graph kernels on 3D skeletal data. Spatial joint features are extracted using joint dis-tances between human joint distributions in 3D space. A spatial graph is constructed using 3D points as vertices and the computed joint distances as edges for each action frame in the video sequence. Spatial graph kernels between the training set and testing set are con-structed to extract similarity between the two action sets. Two spatial graph kernels are constructed with vertex and edge data represented by joint positions and joint distances. To test the proposed method, we use 4 publicly available 3D skeletal datasets from G3D, MSR Action 3D, UT Kinect and NTU RGB+D. The proposed spatial graph kernels result in better classification accuracies compared to the state of the art models.