Human Like Segmentation of Daily Actions based on Switching Model of Linear Dynamical Systems and Human Body Hierarchy

This paper presents a human like segmentation method for daily life actions, such as getting up, sitting down, walking. Unsupervised segmentation methods of many previous researches cannot always assure segmentation result that coincides with human´s natural sense. While the proposed method utilizes human´s teacher data of segmentation to conduct human like segmentation. We assume that latent dynamics changes at the segmentation points of action, and represent segmentation boundary by switching model of two linear dynamic systems. The problem is that human may segment actions according to wide variety of criteria depending on the attention point or other backgrounds. In this paper, those criteria are acquired by clustering segmentation boundaries extracted from teacher data made by human. Each of the cluster is characterized by body parts it pays attention to. Here, we focus on hierarchical aspect of human body that human body can be treated at various levels of abstraction (e.g. whole body, upper body, left arm), and represent it by tree structure. Experimental result shows that the proposed method can acquire human like segmentation criteria