An audio-based sports video segmentation and event detection algorithm

Baillie, M. and Jose, J.M. (2004) An audio-based sports video segmentation and event detection algorithm. In: Computer Vision and Pattern Recognition Workshop, Washington, DC, 27 June - 02 July 2004, p. 110. ISBN 0769521584

Abstract

In this paper, we present an audio-based event detection algorithm shown to be effective when applied to Soccer video. The main benefit of this approach is the ability to recognise patterns that display high levels of crowd response correlated to key events. The soundtrack from a Soccer sequence is first parameterised using Mel-frequency Cepstral coefficients. It is then segmented into homogenous components using a windowing algorithm with a decision process based on Bayesian model selection. This decision process eliminated the need for defining a heuristic set of rules for segmentation. Each audio segment is then labelled using a series of Hidden Markov model (HMM) classifiers, each a representation of one of 6 predefined semantic content classes found in Soccer video. Exciting events are identified as those segments belonging to a crowd cheering class. Experimentation indicated that the algorithm was more effective for classifying crowd response when compared to traditional model-based segmentation and classification techniques.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Jose, Professor Joemon
• Authors: Baillie, M., and Jose, J.M.
• Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Institute of Electrical and Electronics Engineers
• ISBN: 0769521584
• Copyright Holders: Copyright © 2004 Institute of Electrical and Electronics Engineers
• First Published: First published in Proceedings of Computer Vision and Pattern Recognition Workshop
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher