Multi-Modal Audio-Visual Event Recognition for Football Analysis

The recognition of events within multi-modal data is a challenging problem. In this paper we focus on the recognition of events by using both audio and video data. We investigate the use of data fusion techniques in order to recognise these sequences within the framework of Hidden Markov Models (HMM) used to model audio and video data sequences. Specifically we look at the recognition of play and break sequences in football and the segmentation of football games based on these two events. Recognising relatively simple semantic events such as this is an important step towards full automatic indexing of such video material. These experiments were done using approximately 3 hours of data from two games of the Euro96 competition. We propose that modelling the audio and video streams separately for each sequence and fusing the decisions from each stream should yield an accurate and robust method of segmenting multi-modal data.

Published in:
Proc. IEEE Workshop on Neural Networks for Signal Processing (NNSP)
Presented at:
Proc. IEEE Workshop on Neural Networks for Signal Processing (NNSP)
Toulouse, France
IDIAP-RR 03-12

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 Record created 2006-03-10, last modified 2020-07-30

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