Abstract
In this paper, we propose a novel audio-visual feature-based framework for event detection in field sports broadcast video. The system is evaluated via a case-study involving MPEG encoded football video. Specifically, the features gathered by various feature detectors is combined by means of a support vector machine which infers the occurrence of an event based on a model generated during a training phase, utilising a corpus of 2.5 hours of content. The system is evaluated using 2.5 hours of separate test match content.
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