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Automatic Baseball Commentary Generation Using Deep Learning

Title
Automatic Baseball Commentary Generation Using Deep Learning
Author
최용석
Keywords
sports casting; automatic commentary generation; deep learning
Issue Date
2020-03
Publisher
ACM
Citation
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied Computing, Page. 1056-1065
Abstract
Video captioning is known as a method to summarize or explain a video. However, it is very difficult to create sports commentaries, which are running scene-by-scene descriptions, for sports videos by using conventional video captioning. This is the case because sports commentary requires not only specific and varied information about every scene, such as player action descriptions in baseball, but also background knowledge and dynamic at-bats statistics that are not found in the video. We propose a new system to automatically generate commentary for baseball games. In our system, given real-time baseball videos, suitable descriptions are relayed using four deep-learning models (i.e., a scene classifier, player detector, motion recognizer, and pitching result recognizer) integrated with domain ontology. Using these four deep-learning models, pieces of information about "who is doing what in which area of the field" and "what results are expected" are obtained. This approach is used to select an appropriate template, which is combined with baseball ontology knowledge for the generation of commentary. We evaluate our system using baseball games from the KBO (Korea Baseball Organization) League's 2018 season. As a result of the evaluation, we identify that our system1 can act as a commentator for baseball games.
URI
https://dl.acm.org/doi/10.1145/3341105.3374063https://repository.hanyang.ac.kr/handle/20.500.11754/164984
ISBN
978-1-4503-6866-7
DOI
10.1145/3341105.3374063
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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