Abstract

The paper presents a novel approach to real-time event detection in sports broadcasts. We present how the same underlying audio-visual feature extraction algorithm based on new global image descriptors is robust across a range of different sports alleviating the need to tailor it to a particular sport. In addition, we propose and evaluate three different classifiers in order to detect events using these features: a feed-forward neural network, an Elman neural network and a decision tree. Each are investigated and evaluated in terms of their usefulness for real-time event classification. We also propose a ground truth dataset together with an annotation technique for performance evaluation of each classifier useful to others interested in this problem.