کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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525935 | 869043 | 2009 | 10 صفحه PDF | دانلود رایگان |
This paper proposes a novel, trajectory-based approach to the automatic recognition of complex multi-player behavior in a basketball game. First, a probabilistic play model is applied to the player-trajectory data in order to segment the play into game phases (offense, defense, time out). In this way, both the temporal boundaries of the observed activity and its broader context are obtained. Next, the team’s activity is analyzed in more detail by detecting the key elements of basketball play. Following basketball theory, these key elements (starting formation, screen, and move) are the building blocks of basketball play, and therefore their temporal order is used to produce a semantic description of the observed activity. Finally, the activity is recognized by comparing its semantic description with the descriptions of manually defined templates, stored in a database. The effectiveness and robustness of the proposed approach is demonstrated on two championship games and 71 examples of three types of basketball offense.
Journal: Computer Vision and Image Understanding - Volume 113, Issue 5, May 2009, Pages 612–621