期刊
SIGNAL PROCESSING-IMAGE COMMUNICATION
卷 35, 期 -, 页码 35-45出版社
ELSEVIER
DOI: 10.1016/j.image.2015.04.005
关键词
Real-time sports event detection; Neural networks; State machines; Field sports
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 is 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. (C) 2015 Elsevier B.V. All rights reserved.
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