Baillie, M. and Jose, J.M. (2003) Audio-based event detection for sports video. Lecture Notes in Computer Science. ISSN 0302-9743Full text not available in this repository. (Request a copy from the Strathclyde author)
In this paper, we present an audio-based event detection approach shown to be effective when applied to 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 football matches, thus providing a basis for automatic indexing and summarisation.
|Keywords:||information retrieval, learning objects, sport, mel-frequency cepstral coefficients, Electronic computers. Computer science, Theoretical Computer Science, Computer Science(all)|
|Subjects:||Science > Mathematics > Electronic computers. Computer science|
|Department:||Faculty of Science > Computer and Information Sciences|
|Depositing user:||Strathprints Administrator|
|Date Deposited:||28 Mar 2007|
|Last modified:||14 Apr 2017 02:35|