A Symbolic Representation of Motion Capture Data for Behavioral Segmentation

Abstract

For building and understanding computational models of human motion, behavioral segmentation of human motion into actions is a crucial step, which plays an important part in many domains such as motion compression, motion classification and motion analysis. In this paper, we present a novel symbolic representation of human motion capture data, called the Behavior String (BS). Based on the BS, a further motion segmentation algorithm for human motion capture data is proposed. The human motion capture data is treated as a high-dimensional discrete data points, which are clustered by an alternative algorithm based on density, and each cluster is divided into a character. Then, the BS is produced for the motion data by temporal reverting. By analyzing the BS, the human motion capture data is segmented into distinct behavior segments and the cycles of motion are found. Experiments show that our method not only has a good performance in behavioral segmentation for motion capture data, but also finds cycles of motion and the motion clips of the same behaviors from long original motion sequence. Keywords-motion analysis; behavioral segmentation; clustering; motion capture data; cycle