[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.
[Show abstract][Hide abstract] ABSTRACT: Although mosaics are well established as a compact and non-redundant representation of image sequences, their application
still suffers from restrictions of the camera motion or has to deal with parallax errors. We present an approach that allows
construction of mosaics from arbitrary motion of a head-mounted camera pair. As there are no parallax errors when creating
mosaics from planar objects, our approach first decomposes the scene into planar sub-scenes from stereo vision and creates
a mosaic for each plane individually. The power of the presented mosaicing technique is evaluated in an office scenario, including
the analysis of the parallax error.