Highlight generation for basketball video using probabilistic excitement

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.