Chung David H S, Parry Matthew L, Griffiths Iwan W, Laramee Robert S, Bown Rhodri, Legg Philip A, Chen Min
IEEE Comput Graph Appl. 2016 May-Jun;36(3):72-82. doi: 10.1109/MCG.2015.25. Epub 2015 Jan 26.
Organizing sports video data for performance analysis can be challenging, especially in cases involving multiple attributes and when the criteria for sorting frequently changes depending on the user's task. The proposed visual analytic system enables users to specify a sort requirement in a flexible manner without depending on specific knowledge about individual sort keys. The authors use regression techniques to train different analytical models for different types of sorting requirements and use visualization to facilitate knowledge discovery at different stages of the process. They demonstrate the system with a rugby case study to find key instances for analyzing team and player performance. Organizing sports video data for performance analysis can be challenging in cases with multiple attributes, and when sorting frequently changes depending on the user's task. As this video shows, the proposed visual analytic system allows interactive data sorting and exploration. https://youtu.be/Cs6SLtPVDQQ.
为进行性能分析而整理体育视频数据可能具有挑战性,特别是在涉及多个属性的情况下,以及排序标准经常根据用户任务而变化时。所提出的视觉分析系统使用户能够灵活地指定排序要求,而无需依赖关于各个排序键的特定知识。作者使用回归技术为不同类型的排序要求训练不同的分析模型,并使用可视化来促进该过程不同阶段的知识发现。他们通过一个橄榄球案例研究展示了该系统,以找到用于分析团队和球员表现的关键实例。在具有多个属性的情况下,以及当排序频繁根据用户任务变化时,为进行性能分析而整理体育视频数据可能具有挑战性。正如本视频所示,所提出的视觉分析系统允许交互式数据排序和探索。https://youtu.be/Cs6SLtPVDQQ。