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@article{ART002748133},
author={Jun-Young Park and Jae-Seung Kim and Yong-Tae Woo},
title={A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2021},
volume={26},
number={8},
pages={31-37},
doi={10.9708/jksci.2021.26.08.031}

TY - JOUR
AU - Jun-Young Park
AU - Jae-Seung Kim
AU - Yong-Tae Woo
TI - A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model
JO - Journal of The Korea Society of Computer and Information
PY - 2021
VL - 26
IS - 8
PB - The Korean Society Of Computer And Information
SP - 31
EP - 37
SN - 1598-849X
AB - 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.
KW - Deep Learning;Convolutional Neural Network;Scene classification and retrieval
DO - 10.9708/jksci.2021.26.08.031
ER -

Jun-Young Park, Jae-Seung Kim and Yong-Tae Woo. (2021). A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model. Journal of The Korea Society of Computer and Information, 26(8), 31-37.

Jun-Young Park, Jae-Seung Kim and Yong-Tae Woo. 2021, "A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model", Journal of The Korea Society of Computer and Information, vol.26, no.8 pp.31-37. Available from: doi:10.9708/jksci.2021.26.08.031

Jun-Young Park, Jae-Seung Kim, Yong-Tae Woo "A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model" Journal of The Korea Society of Computer and Information 26.8 pp.31-37 (2021) : 31.

Jun-Young Park, Jae-Seung Kim, Yong-Tae Woo. A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model. 2021; 26(8), 31-37. Available from: doi:10.9708/jksci.2021.26.08.031

Jun-Young Park, Jae-Seung Kim and Yong-Tae Woo. "A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model" Journal of The Korea Society of Computer and Information 26, no.8 (2021) : 31-37.doi: 10.9708/jksci.2021.26.08.031

Jun-Young Park; Jae-Seung Kim; Yong-Tae Woo. A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model. Journal of The Korea Society of Computer and Information, 26(8), 31-37. doi: 10.9708/jksci.2021.26.08.031

Jun-Young Park; Jae-Seung Kim; Yong-Tae Woo. A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model. Journal of The Korea Society of Computer and Information. 2021; 26(8) 31-37. doi: 10.9708/jksci.2021.26.08.031

Jun-Young Park, Jae-Seung Kim, Yong-Tae Woo. A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model. 2021; 26(8), 31-37. Available from: doi:10.9708/jksci.2021.26.08.031

Jun-Young Park, Jae-Seung Kim and Yong-Tae Woo. "A motion classification and retrieval system in baseball sports video using Convolutional Neural Network model" Journal of The Korea Society of Computer and Information 26, no.8 (2021) : 31-37.doi: 10.9708/jksci.2021.26.08.031