Sports Video Analysis

Sports Video Analysis

Hua-Tsung Chen (National Chiao-Tung University, Taiwan) and Suh-Yin Lee (National Chiao-Tung University, Taiwan)
DOI: 10.4018/978-1-61692-859-9.ch012


The explosive proliferation of multimedia data necessitates the development of automatic systems and tools for content-based multimedia analysis. Recently, sports video analysis has been attracting more and more attention due to the potential commercial benefits, entertaining functionalities and mass audience requirements. Much research on shot classification, highlight extraction and event detection in sports video has been done to provide the general audience interactive video viewing systems for quick browsing, indexing and summarization. More keenly than ever, the audience desire professional insights into the games. The coach and the players demand automatic tactics analysis and performance evaluation with the aid of multimedia information retrieval technologies. It is also a growing trend to provide computer-assisted umpiring in sports games, such as the well-known Hawk eye system used in tennis. Therefore, sports video analysis is certainly a research issue worth investigation. In this chapter, the authors propose to review current research and give an insight into sports video analysis. The discussion on potential applications and encouraging future work is also presented.
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The contents of sports video are well-structured since the broadcasters present the game process in similar ways due to the game rules. Therefore, many domain-specific features and knowledge can be employed and incorporated into sports video analysis. The possible applications have been found in many kinds of sports, e.g., baseball, soccer, tennis, volleyball, etc. The major research issues are described as follows.

Shot Classification

In a sports game, the positions of cameras are fixed around the field and each camera has a specific assignment for broadcasting the game. The rules of presenting the game progress are similar in different channels. The broadcasting technique that a few dominant shots constitute most parts of a sports game leads to the requirement of shot classification. Duan et al. (2003 & 2005) employ a supervised learning scheme to perform a top-down shot classification based on mid-level representations, including motion vector field model, color tracking model and shot pace model. Hua et al. (2002) integrate color distribution, edge distribution, camera motion, sound effects and closed captions with maximum entropy scheme to classify baseball scenes. Kumano et al. (2005) divide a frame into blocks and analyze the mean, variance and log variance of the luminosity within each block for pitch scene discrimination. Lu and Tan (2003) propose a recursive peer-group filtering scheme to identify prototypical shots for each dominant scene, and examine time coverage of these prototypical shots to decide the number of dominant scenes for each sports video. Mochizuki et al. (2005) provide a baseball indexing method based on patternizing baseball scenes using a set of rectangles with some image features and a motion vector.

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