Audio-based event detection for sports video

Baillie, M. and Jose, J.M. (2003) Audio-based event detection for sports video. Lecture Notes in Computer Science, 2728, pp. 61-65.

Abstract

In this paper, we present an audio-based event detection approach shown to be effective when applied to the Sports broadcast data. The main benefit of this approach is the ability to recognise patterns that indicate high levels of crowd response which can be correlated to key events. By applying Hidden Markov Model-based classifiers, where the predefined content classes are parameterised using Mel-Frequency Cepstral Coefficients, we were able to eliminate the need for defining a heuristic set of rules to determine event detection, thus avoiding a two-class approach shown not to be suitable for this problem. Experimentation indicated that this is an effective method for classifying crowd response in Soccer matches, thus providing a basis for automatic indexing and summarisation.

• Item Type: Articles
• Additional Information: The original publication is available at www.springerlink.com
• 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
• Journal Name: Lecture Notes in Computer Science
• Publisher: Springer
• ISSN: 1611-3349
• Copyright Holders: Copyright © 2003 Springer
• First Published: First published in Lecture Notes in Computer Science 2728:61-65
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher