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Proceedings Paper

Temporal structure analysis of broadcast tennis video using hidden Markov models
Author(s): Ewa Kijak; Lionel Oisel; Patrick Gros
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Paper Abstract

This work aims at recovering the temporal structure of a broadcast tennis video from an analysis of the raw footage. Our method relies on a statistical model of the interleaving of shots, in order to group shots into predefined classes representing structural elements of a tennis video. This stochastic modeling is performed in the global framework of Hidden Markov Models (HMMs). The fundamental units are shots and transitions. In a first step, colors and motion attributes of segmented shots are used to map shots into 2 classes: game (view of the full tennis court) and not game (medium, close up views, and commercials). In a second step, a trained HMM is used to analyze the temporal interleaving of shots. This analysis results in the identification of more complex structures, such as first missed services, short rallies that could be aces or services, long rallies, breaks that are significant of the end of a game and replays that highlight interesting points. These higher-level unit structures can be used either to create summaries, or to allow non-linear browsing of the video.

Paper Details

Date Published: 10 January 2003
PDF: 11 pages
Proc. SPIE 5021, Storage and Retrieval for Media Databases 2003, (10 January 2003); doi: 10.1117/12.476258
Show Author Affiliations
Ewa Kijak, Thomson Multimedia (France)
IRISA (France)
Lionel Oisel, Thomson Multimedia (France)
Patrick Gros, IRISA-CNRS (France)

Published in SPIE Proceedings Vol. 5021:
Storage and Retrieval for Media Databases 2003
Minerva M. Yeung; Rainer W. Lienhart; Chung-Sheng Li, Editor(s)

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