Human Pose Estimation from Volume Data and Topological Graph Database

Abstract. This paper proposes a novel volume-based motion capture method using a bottom-up analysis of volume data and an example topology database of the human body. By using a two-step graph match-ing algorithm with many example topological graphs corresponding to postures that a human body can take, the proposed method does not require any initial parameters or iterative convergence processes, and it can solve the changing topology problem of the human body. First, three-dimensional curved lines (skeleton) are extracted from the captured volume data using the thinning process. The skeleton is then converted into an attributed graph. By using a graph matching algorithm with a large amount of example data, we can identify the body parts from each curved line in the skeleton. The proposed method is evaluated using sev-eral video sequences of a single person and multiple people, and we can confirm the validity of our approach.