Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks
Teaching Machines to Understand Baseball Games: Large-Scale Baseball Video Database for Multiple Video Understanding Tasks
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DOI：
10.1007/978-3-030-01267-0_25
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发表时间：
2018-09
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影响因子：
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通讯作者：
Minho Shim;Young Hwi Kim;Kyungmin Kim;Seon Joo Kim
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文献类型：
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作者：
Minho Shim;Young Hwi Kim;Kyungmin Kim;Seon Joo Kim
教机器理解视频的一个主要障碍是缺乏训练数据，因为为长视频创建时间注释需要大量的人力。为此，我们引入了一个新的大规模棒球视频数据集，称为BBDB，这是通过使用在线提供的逐场比赛文本半自动生成的。BBDB包含4200小时的棒球比赛视频，其中有400 k个时间注释的活动片段。与其他数据集相比，新数据集具有几个主要的挑战性因素：1）数据集包含大量具有不同标签的视觉相似片段。2)它可以用于许多视频理解任务，包括视频识别，定位，文本-视频对齐，视频亮点生成和数据不平衡问题。为了观察BBDB的潜力，我们通过在我们的新数据集上运行许多不同类型的视频理解算法进行了广泛的实验。该数据库可在https：//sites上查阅。Google. www.eccv2018bbdb.com
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/