Teaching Machines to Understand Baseball Games:
Large-Scale Baseball Video Database for
Multiple Video Understanding Tasks
Abstract. A major obstacle in teaching machines to understand videos is the
lack of training data, as creating temporal annotations for long videos requires
a huge amount of human effort. To this end, we introduce a new large-scale
baseball video dataset called the BBDB, which is produced semi-automatically
by using play-by-play texts available online. The BBDB contains 4200 hours
of baseball game videos with 400k temporally annotated activity segments. The
new dataset has several major challenging factors compared to other datasets:
1) the dataset contains a large number of visually similar segments with different labels. 2) It can be used for many video understanding tasks including
video recognition, localization, text-video alignment, video highlight generation,
and data imbalance problem. To observe the potential of the BBDB, we conducted extensive experiments by running many different types of video understanding algorithms on our new dataset. The database is available at https:
//sites.google.com/site/eccv2018bbdb/