  The popularity of racket sports (e.g., tennis and table tennis) leads to high
demands for data analysis, such as notational analysis, on player performance.
While sports videos offer many benefits for such analysis, retrieving accurate
information from sports videos could be challenging. In this paper, we propose
EventAnchor, a data analysis framework to facilitate interactive annotation of
racket sports video with the support of computer vision algorithms. Our
approach uses machine learning models in computer vision to help users acquire
essential events from videos (e.g., serve, the ball bouncing on the court) and
offers users a set of interactive tools for data annotation. An evaluation
study on a table tennis annotation system built on this framework shows
significant improvement of user performances in simple annotation tasks on
objects of interest and complex annotation tasks requiring domain knowledge.
