Teaching machines to understand baseball games: Large-scale baseball video database for multiple video understanding tasks

Minho Shim, Young Hwi Kim, Kyungmin Kim, Seon Joo Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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+hr 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/.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings
EditorsYair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert
PublisherSpringer Verlag
Number of pages18
ISBN (Print)9783030012663
Publication statusPublished - 2018
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sept 82018 Sept 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11219 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other15th European Conference on Computer Vision, ECCV 2018

Bibliographical note

Funding Information:
Acknowledgment. This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-01.

Publisher Copyright:
© Springer Nature Switzerland AG 2018.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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