서지주요정보
Deformable parts model based player identification using deep convolutional representations = 심층회선 표현을 이용한 변형부분모델 기반 선수 판별
서명 / 저자 Deformable parts model based player identification using deep convolutional representations = 심층회선 표현을 이용한 변형부분모델 기반 선수 판별 / Arda Senocak.
저자명 Senocak, Arda ; Senocak Arda
발행사항 [대전 : 한국과학기술원, 2015].
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소장정보

등록번호

8028245

소장위치/청구기호

학술문화관(문화관) 보존서고

MEE 15112

휴대폰 전송

도서상태

이용가능

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초록정보

Intelligent and interactive sports video analysis systems have shown significant progress in recent years. However, most of the improvements are done in detection and tracking algorithms. This thesis work addresses the problem of automatic player identification system in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this settings is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires more sophisticated approaches to capture distinctive features from players to distinguish them. For reliable identification system, it is necessary to find some features with high level semantic meanings. Because players’ appearance is very similar and confusing. To this end, we use powerful Convolutional Neural Networks (CNN) features with richer information extracted at multiple scales and encode them with Fisher Vector method which has ability to capture and magnify the small differences. We also investigate the distinguishing parts of the players and present Deformable Part Model (DPM) based pooling approach to use these distinctive feature points. The resulting image representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.

Intelligent and interactive sports video analysis systems have shown significant progress in recent years. However, most of the improvements are done in detection and tracking algorithms. This thesis work addresses the problem of automatic player identification system in broadcast sports videos filmed with a single side-view medium distance camera. Player identification in this settings is a challenging task because visual cues such as faces and jersey numbers are not clearly visible. Thus, this task requires more sophisticated approaches to capture distinctive features from players to distinguish them. For reliable identification system, it is necessary to find some features with high level semantic meanings. Because players’ appearance is very similar and confusing. To this end, we use powerful Convolutional Neural Networks (CNN) features with richer information extracted at multiple scales and encode them with Fisher Vector method which has ability to capture and magnify the small differences. We also investigate the distinguishing parts of the players and present Deformable Part Model (DPM) based pooling approach to use these distinctive feature points. The resulting image representation is able to identify players even in difficult scenes. It achieves state-of-the-art results up to 96% on NBA basketball clips.

서지기타정보

서지기타정보
청구기호 {MEE 15112
형태사항 v,39 : 삽도 ; 30 cm
언어 영어
일반주기 저자명의 한글표기 : Senocak Arda
지도교수의 영문표기 : In So Kweon
지도교수의 한글표기 : 권인소
Including Appendix
학위논문 학위논문(석사) - 한국과학기술원 : 전기및전자공학부,
서지주기 References : p.
주제 Player Identification
Sports Videos
Deformable Parts Model
Deep Learning
Image Classification
심층회선
변형부분모델
이용한
판별
기반 선수
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