A combined Bayesian Markovian approach for behaviour recognitionCarter, N., Young, D. and Ferryman, J. (2006) A combined Bayesian Markovian approach for behaviour recognition. In: Tang, Y. Y., Wang, S. P., Lorette, G., Yeung, D. S. and Yan, H. (eds.) 18th International Conference on Pattern Recognition, Vol 1, Proceedings. International Conference on Pattern Recognition. IEEE Computer Soc, Los Alamitos, pp. 761-764. ISBN 1051-4651 0-7695-2521-0 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryNumerous techniques exist which can be used for the task of behavioural analysis and recognition. Common amongst these are Bayesian networks and Hidden Markov Models. Although these techniques are extremely powerful and well developed, both have important limitations. By fusing these techniques together to form Bayes-Markov chains, the advantages of both techniques can be preserved, while reducing their limitations. The Bayes-Markov technique forms the basis of a common, flexible framework for supplementing Markov chains with additional features. This results in improved user output, and aids in the rapid development of flexible and efficient behaviour recognition systems.
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