Name
Abstract | ||
|---|---|---|
Organizing sport video data for performance analysis can be challenging, especially when this involves multiple attributes, and the criteria for sorting frequently changes depending on the user's task. In this work, we propose a visual analytic system to convert a user's knowledge on rankings to support such a process. The system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. We use regression techniques to train different analytical models for different types of sorting requirements. We use visualization to facilitate the discovery of knowledge at different stages of the visual analytic process. This includes visualizing the parameters of the ranking model, visualizing the results of a sort query for interactive exploration, and the playback of sorted video clips. We demonstrate the system with a case study in rugby to find key instances for analyzing team and player performance. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MCG.2015.25 | IEEE Computer Graphics and Applications |
Field | DocType | Volume |
Computer vision,Data modeling,Ranking,Visualization,Computer science,sort,Visual analytics,Sorting,Knowledge extraction,Artificial intelligence,Multimedia,Computer graphics | Journal | PP |
Issue | ISSN | Citations |
99 | 1558-1756 | 3 |
PageRank | References | Authors |
0.36 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| David H. S. Chung | 1 | 100 | 5.34 |
| Matthew L. Parry | 2 | 63 | 4.15 |
| Iwan W. Griffiths | 3 | 63 | 4.15 |
| Robert S. Laramee | 4 | 1405 | 85.31 |
| Rhodri Bown | 5 | 3 | 0.36 |
| Philip A. Legg | 6 | 156 | 11.55 |
| Min Chen | 7 | 1293 | 82.69 |