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.
|Publication status||Published - 2004|
|Event||IEEE Workshop on Event Mining 2004: IEEE Computer Vision and Pattern Recognition - Washington DC, United States|
Duration: 2 Jul 2004 → …
|Conference||IEEE Workshop on Event Mining 2004: IEEE Computer Vision and Pattern Recognition|
|Abbreviated title||CVPR 2004|
|Period||2/07/04 → …|
- event detection algorithm
- Mel-frequency Cepstral coefficients
- Bayesian model
- information retieval
Baillie, M., & Jose, J. M. (2004). An Audio-based sports video segmentation and event detection algorithm. Paper presented at IEEE Workshop on Event Mining 2004: IEEE Computer Vision and Pattern Recognition , Washington DC, United States.