Name
Abstract | ||
|---|---|---|
Sports broadcasts often use pitch aligned graphics to provide additional information to the viewer. This is often achieved by using professional cameras equipped with high accuracy sensors or elaborate manual calibration techniques to measure the broadcasting cameras' position and orientation, allowing the graphics to be accurately matched to the camera view. While previous research has investigated how the camera position and orientation can be estimated for professional broadcast cameras alone, none of the previous works have targeted smartphones.In this paper, we investigate whether line pitch markings in combination with feature matching computer vision techniques can be used to estimate an on-site users position and orientation with sufficient accuracy to align augmented reality content with the pitch. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/IVCNZ48456.2019.8961006 | 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ) |
Keywords | Field | DocType |
smartphones,broadcasting cameras position,sport events,augmented reality content,on-site users position,feature matching computer vision techniques,line pitch markings,professional broadcast cameras,camera view,calibration techniques,high accuracy sensors,professional cameras,pitch aligned graphics,sports broadcasts | Graphics,Computer vision,Dot pitch,Broadcasting,Computer science,Augmented reality,Feature matching,Artificial intelligence | Conference |
ISSN | ISBN | Citations |
2151-2191 | 978-1-7281-4188-6 | 0 |
PageRank | References | Authors |
0.34 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Skinner | 1 | 0 | 0.68 |
| Stefanie Zollmann | 2 | 227 | 22.58 |