On Multiscale Self-Similarities Description for Effective Three-Dimensional/Six-Dimensional Motion Trajectory Recognition

Abstract

Motion trajectories provide compact informative clues in characterizing motion behaviors of human bodies, robots, and moving objects. This paper devises an invariant and unified descriptor for three-dimensional/six-dimensional (3-D/6-D) motion trajectories recognition by exploring the latent motion patterns in the multiscale self-similarity matrices (MSM) within a motion trajectory and its components. The MSM approach transforms a motion trajectory in Euclidean space into a set of similarity matrices and exhibits strong invariances, in which each matrix can be regarded as a grayscale image. Next, the histograms of oriented gradients features extracted from the MSM representation are concatenated as the final trajectory descriptor. In addition, an improved kernel MSM is raised by calculating the pairwise kernel distances. Finally, extensive 3-D/6-D motion trajectory recognition experiments on three public datasets with a linear support vector machine classifier are conducted to verify the effectiveness and efficiency of the proposed approach.