Paper Info
Title
Highlight generation for basketball video using probabilistic excitement
Abstract
With popularization of multimedia devices, semantic analysis of sports video has been widely studied. In this paper, we propose a highlight generation method for basketball games. To create a video highlight, the proposed method selects interesting shots by modeling excitements of the game using score information. For this purpose, a video is first segmented into shots and classified as play and nonplay shots. At the same time, score of the game is automatically extracted from video frames. To select interesting shots, which should be included to the highlight video, excitement of the game is estimated from the variation of game scores. Unlike previous event-based video analysis methods which focus on individual events, our proposed method is able to reflect the contents of a game by considering excitements. Also, because the excitement modeling uses score information only, it can easily be extended to other types of sports video which have scores.
Year
DOI
Venue
2009
10.1109/ICME.2009.5202499
ICME
Keywords
Field
DocType
basketball game,video highlight,interesting shot,score information,probabilistic excitement,game score,sports video,previous event-based video analysis,highlight video,video frame,basketball video,feature extraction,data mining,probability,sport,image classification,probabilistic logic,image segmentation,games
Computer vision,Computer science,Basketball games,Image segmentation,Feature extraction,Artificial intelligence,Probabilistic logic,Contextual image classification,Basketball
Conference
ISSN
Citations 
PageRank 
1945-7871
3
0.41
References 
Authors
7
3
Name
Order
Citations
PageRank
Gwang-Gook Lee1474.63
Hyeong-ki Kim230.41
Whoi-Yul Kim351847.84