Statistical Framework for Shot Segmentation and Classification in Sports Video

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Statistical Framework for Shot Segmentation and Classification in Sports Video

Yang, Ying / Lin, Shouxun / Zhang, Yongdong / Tang, Sheng

In this paper, a novel statistical framework is proposed for shot segmentation and classification. The proposed framework segments and classifies shots simultaneously using same difference features based on statistical inference. The task of shot segmentation and classification is taken as finding the most possible shot sequence given feature sequences, and it can be formulated by a conditional probability which can be divided into a shot sequence probability and a feature sequence probability. Shot sequence probability is derived from relations between adjacent shots by Bi-gram, and feature sequence probability is dependent on inherent character of shot modeled by HMM. Thus, the proposed framework segments shot considering the character of intra-shot to classify shot, while classifies shot considering character of inter-shot to segment shot, which obtain more accurate results. Experimental results on soccer and badminton videos are promising, and demonstrate the effectiveness of the proposed framework.

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Document information

Title :

Statistical Framework for Shot Segmentation and Classification in Sports Video

Contributors:

Yagi, Yasushi (editor) / Kang, Sing Bing (editor) / Kweon, In So (editor) / Zha, Hongbin (editor) / Yang, Ying (author) / Lin, Shouxun (author) / Zhang, Yongdong (author) / Tang, Sheng (author)

Conference:

Asian Conference on Computer Vision ; 2007 ; Tokyo, Japan November 18, 2007 - November 22, 2007

Published in:

Computer Vision – ACCV 2007 ; Chapter : 11 ; 106-115

Lecture Notes in Computer Science ; 4844

Publication date :

2007-01-01

Size :

10 pages

ISBN :

978-3-540-76390-1 , 978-3-540-76389-5

ISSN :