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/.
|Title of host publication||Computer Vision – ECCV 2018 - 15th European Conference, 2018, Proceedings|
|Editors||Yair Weiss, Vittorio Ferrari, Cristian Sminchisescu, Martial Hebert|
|Number of pages||18|
|Publication status||Published - 2018|
|Event||15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany|
Duration: 2018 Sep 8 → 2018 Sep 14
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||15th European Conference on Computer Vision, ECCV 2018|
|Period||18/9/8 → 18/9/14|
Bibliographical noteFunding Information:
Acknowledgment. This work was supported by Samsung Research Funding Center of Samsung Electronics under Project Number SRFC-IT1701-01.
© Springer Nature Switzerland AG 2018.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)