본문 바로가기
  • Home

A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(8), pp.31-37
  • DOI : 10.9708/jksci.2021.26.08.031
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : July 26, 2021
  • Accepted : August 23, 2021
  • Published : August 31, 2021

Jun-Young Park 1 Jae-Seung Kim 1 Yong-Tae Woo 1

1창원대학교

Accredited

ABSTRACT

In this paper, we propose a method to effectively search by automatically classifying scenes in which specific images such as pitching or swing appear in baseball game images using a CNN(Convolution Neural Network) model. In addition, we propose a video scene search system that links the classification results of specific motions and game records. In order to test the efficiency of the proposed system, an experiment was conducted to classify the Korean professional baseball game videos from 2018 to 2019 by specific scenes. In an experiment to classify pitching scenes in baseball game images, the accuracy was about 90% for each game. And in the video scene search experiment linking the game record by extracting the scoreboard included in the game video, the accuracy was about 80% for each game. It is expected that the results of this study can be used effectively to establish strategies for improving performance by systematically analyzing past game images in Korean professional baseball games.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.