Real-time event classification in field sport videos

•Article presents novel system for real-time video indexing for field sports videos based on the new video description technique.•Up to date there is no system that covers so many different sports genres, features so high accuracy and is capable of working in real-time.•Several classifiers were compared in the results section which gives additional knowledge about the system features.

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.