Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Automatic Classification Technique of Offence Pattern in Soccer Game using Neural Networks

뉴럴네트워크를 이용한 축구경기에 있어서의 공격패턴 자동분류 기법

  • 김현숙 (신성대학 컴퓨터계열) ;
  • 김광용 (한국전자통신연구원 영상처리연구부) ;
  • 남성현 (두원공과대학 소프트웨어개발과) ;
  • 황종선 (고려대학교 컴퓨터학과) ;
  • 양영규 (한국전자통신연구원 영상처리연구부)
  • Published : 2000.07.15
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Abstract

In this paper, we suggest and test a classification technique of offence pattern from group formation to automatically index highlights of soccer games. A BP (Back-propagation) neural nets technique was applied to the information of the position of both the player and the ball on a ground, and the distance between the player and the ball to identify the group formation in space and time. The real soccer game scenes including '98 France World Cup were used to extract 297 video clips of various types of offence patterns; Left Running 60, Right Running 74, Center Running 72, Corner-kick 39 and Free-kick 52. The results are as follows: Left Running comes to 91.7%, Right Running 100%. Center Running 87.5%, Corner-kick 97.4% and Free-kick 75%, and these showed quite a satisfactory rate of recognition.

본 논문은 팀 스포츠(team sports)의 일종인 축구경기 하이라이트 장면의 자동색인을 위해 뉴럴네트워크 기법을 이용하여 그룹 포메이션(group formation) 중의 공격패턴 자동분류 기법을 개발하고 이를 검증하였다. 본 연구에서는 축구경기의 대표 프레임 상에서 선수들과 공의 위치정보를 추출하고 그룹 포메이션 정보를 기초로 뉴럴네트워크의 BP(Back-propagation) 알고리즘을 사용하여 축구경기 하이라이트 장면의 자동추출을 위한 공격패턴 자동분류 기법을 개발 및 검증하였다. 또한, 실험에는 ‘98 프랑스 월드컵 축구경기의 다양한 공격패턴에 대한 비디오 영상에서 각각 좌측공격 60개, 우측공격 74개, 중앙공격 72, 코너킥 39, 프리킥 52개의 총 297 개의 데이타를 추출하여 사용하였다. 실험결과는 좌측공격 91.7%, 우측공격 100%, 중앙공격 87.5%. 코너킥 97.4%, 프리킥 75% 로서 매우 양호한 인식율을 보였다.

Keywords

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