Taekwondo Poomsae performance is a series of basic movements for offense and defense techniques. Despite the high popularity and long history of Taekwondo, there has been less effort to systemize Taekwondo Poomsae competition. In this paper, we propose a hierarchical approach to landmarks detection in Taekwondo Poomsae videos, which is a significant change in a series of movements. First, we propose a kinematic model for basic Poomsae movements based on the anatomic analysis of player's body parts. Second, we measure a player's movement from a Poomsae video using changed pixels. Third, we segment a Poomsae video into a number of movements, each of which contains the same semantic, i.e., basic Poomsae movement. Since the initial segments are usually over- segmented, we classify the segmented movements into higher level that represent significant movements of Poomsae performance. Finally, we identify landmarks from the created movement hierarchy. The experimental results show that the 70% of landmarks are detected correctly.
['Munther Abualkibash', 'James Gedney', 'Yongbom Kim', 'Jeongkyu Lee']
A Hierarchical Approach to Landmarks Detection in Taekwondo Poomsae Videos