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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: IEEE Workshop on Event Mining 2004: IEEE Computer Vision and Pattern Recognition, 2004-07-02, Washington DC.

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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 or Workshop Item (Paper)
ID code: 2731
Keywords: event detection algorithm, sport, football, Mel-frequency Cepstral coefficients, Bayesian model, classification, information retieval, Electronic computers. Computer science
Subjects: Science > Mathematics > Electronic computers. Computer science
Department: Faculty of Science > Computer and Information Sciences
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 03 Apr 2007
    Last modified: 17 Jul 2013 14:54
    URI: http://strathprints.strath.ac.uk/id/eprint/2731

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