Deformation Pattern Recognition of Point-cloud using Feature Flow Similarity

Abstract

Most current motion recognition technologies, such as motion capture, track feature points such as joints and analyze changes in their coordinates. However, such methods cannot be applied to creatures or objects with complex deformation patterns that are difficult to track feature points. In this report, we propose a method for analyzing deformation patterns using changes in the frequency distribution of shape feature values of a 3D point cloud in order to recognize motion of non-rigid objects for which feature point tracking is difficult. Experimental results show that the self-similarity matrix can be used to segment repeated deformation patterns, and that it can be applied to deformation pattern retrieval, period analysis, and process discrimination by creating a similarity matrix between the discovered pattern and the object to be analyzed.