Automatic soccer video analysis and summarization

We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level processing algorithms, such as dominant color region detection, robust shot boundary de...

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Bibliographic Details
Published in:IEEE transactions on image processing 2003-07, Vol.12 (7), p.796-807
Main Authors: Ekin, A., Tekalp, A.M., Mehrotra, R.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Color
Computer vision
Event detection
Exact sciences and technology
Games
Gunshot detection systems
Image processing
Information, signal and communications theory
IP networks
Narrowband
Pattern recognition
Production
Robustness
Shape
Signal processing
Soccer
Studies
Telecommunications and information theory
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Summary:We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game; ii) all goals in a game; iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g., goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured in different countries and under different conditions.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2003.812758