Motion Segmentation Using Central Distance Features and Low-Pass Filter

Abstract

The motion segmentation is to divide the original motion sequence into several motion fragments with specific semantic, which plays an important role in the motion compression, motion classification, motion synthesis. This paper presents a motion segmentation algorithm based on the central distance features and low-pass filter for the human motion capture data. The proposed approach mainly includes three steps. Firstly, a set of central distance features from the center joint ROOT to limbs was extracted, and those features were divided into the upper and lower limbs norms. Then, PCA method was used to get the one dimension principal component, which can better represent the original motion. Furthermore, the low-pass filter is utilized to get the denoising signal. Consequently, the segmental points set can be obtained. Experimental results show the promising performance of our algorithm.