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Proceedings Paper

Multilevel analysis of sports video sequences
Author(s): Jungong Han; Dirk Farin; Peter H. N. de With
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Paper Abstract

We propose a fully automatic and flexible framework for analysis and summarization of tennis broadcast video sequences, using visual features and specific game-context knowledge. Our framework can analyze a tennis video sequence at three levels, which provides a broad range of different analysis results. The proposed framework includes novel pixel-level and object-level tennis video processing algorithms, such as a moving-player detection taking both the color and the court (playing-field) information into account, and a player-position tracking algorithm based on a 3-D camera model. Additionally, we employ scene-level models for detecting events, like service, base-line rally and net-approach, based on a number real-world visual features. The system can summarize three forms of information: (1) all court-view playing frames in a game, (2) the moving trajectory and real-speed of each player, as well as relative position between the player and the court, (3) the semantic event segments in a game. The proposed framework is flexible in choosing the level of analysis that is desired. It is effective because the framework makes use of several visual cues obtained from the real-world domain to model important events like service, thereby increasing the accuracy of the scene-level analysis. The paper presents attractive experimental results highlighting the system efficiency and analysis capabilities.

Paper Details

Date Published: 16 January 2006
PDF: 12 pages
Proc. SPIE 6073, Multimedia Content Analysis, Management, and Retrieval 2006, 607303 (16 January 2006); doi: 10.1117/12.648849
Show Author Affiliations
Jungong Han, Univ. of Technology Eindhoven (Netherlands)
Dirk Farin, Univ. of Technology Eindhoven (Netherlands)
Peter H. N. de With, Univ. of Technology Eindhoven (Netherlands)
LogicaCMG (Netherlands)

Published in SPIE Proceedings Vol. 6073:
Multimedia Content Analysis, Management, and Retrieval 2006
Edward Y. Chang; Alan Hanjalic; Nicu Sebe, Editor(s)

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