Manifold Warp Segmentation of Human Action

Human action segmentation is important for human action analysis, which is a highly active research area. Most segmentation methods are based on clustering or numerical descriptors, which are only related to data, and consider no relationship between the data and physical characteristics of human actions. Physical characteristics of human motions are those that can be directly perceived by human beings, such as speed, acceleration, continuity, and so on, which are quite helpful in detecting human motion segment points. We propose a new physical-based descriptor of human action by curvature sequence warp space alignment (CSWSA) approach for sequence segmentation in this paper. Furthermore, time series-warp metric curvature segmentation method is constructed by the proposed descriptor and CSWSA. In our segmentation method, descriptor can express the changes of human actions, and CSWSA is an auxiliary method to give suggestions for segmentation. The experimental results show that our segmentation method is effective in both CMU human motion and video-based data sets.

I. Introduction

Human motion sequence contains rich and delicate natural information; it is now widely used in video and animation production, physical training, rehabilitative training, and so on. In the process of human motion capture, the motion sequence should be segmented into meaningful primitives, which is used for guaranteeing that motion clips have the meaning of specific semantics. After that, these motion clips can be used in human motion synthesis [1], recognition, retrieval [2], [3], and prediction [43].