Automatic baseball commentary generation using deep learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Byeong Jo | - |
dc.contributor.author | Choi, Yong Suk | - |
dc.date.accessioned | 2022-07-08T09:24:49Z | - |
dc.date.available | 2022-07-08T09:24:49Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020-03 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/146036 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | Automatic baseball commentary generation using deep learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Yong Suk | - |
dc.identifier.doi | 10.1145/3341105.3374063 | - |
dc.identifier.scopusid | 2-s2.0-85083026004 | - |
dc.identifier.bibliographicCitation | Proceedings of the ACM Symposium on Applied Computing, pp.1056 - 1065 | - |
dc.relation.isPartOf | Proceedings of the ACM Symposium on Applied Computing | - |
dc.citation.title | Proceedings of the ACM Symposium on Applied Computing | - |
dc.citation.startPage | 1056 | - |
dc.citation.endPage | 1065 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Baseball | - |
dc.subject.keywordPlus | Learning systems | - |
dc.subject.keywordPlus | Motion estimation | - |
dc.subject.keywordPlus | Ontology | - |
dc.subject.keywordPlus | Back-ground knowledge | - |
dc.subject.keywordPlus | Baseball Games | - |
dc.subject.keywordPlus | Baseball videos | - |
dc.subject.keywordPlus | Domain ontologies | - |
dc.subject.keywordPlus | Learning models | - |
dc.subject.keywordPlus | Player action | - |
dc.subject.keywordPlus | Scene description | - |
dc.subject.keywordPlus | Sports video | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordAuthor | Automatic commentary generation | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Sports casting | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3341105.3374063 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.