[show abstract][hide abstract] ABSTRACT: The determination of the player’s gestures and actions in sports video is a key task in automating the analysis of the video
material at a high level. In many sports views, the camera covers a large part of the sports arena, so that the resolution
of player’s region is low. This makes the determination of the player’s gestures and actions a challenging task, especially
if there is large camera motion. To overcome these problems, we propose a method based on curvature scale space templates
of the player’s silhouette. The use of curvature scale space makes the method robust to noise and our method is robust to
significant shape corruption of a part of player’s silhouette. We also propose a new recognition method which is robust to
noisy sequences of data and needs only a small amount of training data.