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Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model | IEEE Conference Publication | IEEE Xplore

Unsupervised trajectory pattern classification using hierarchical Dirichlet Process Mixture hidden Markov model


Abstract:

In this paper we present a trajectory clustering method based on nonparametric Bayesian approach proposed for analyzing dynamic systems. Our method uses a modified hierar...Show More

Abstract:

In this paper we present a trajectory clustering method based on nonparametric Bayesian approach proposed for analyzing dynamic systems. Our method uses a modified hierarchical Dirichlet process-hidden Markov model in order to learn trajectory patterns into its parameter variables in an unsupervised way. Due to inherited Bayesian structure, this model resolves some limitations in trajectory clustering problem such as sequential analysis, incremental learning and non-uniform sampling. In this paper we introduce this model and its learning algorithm and finally we evaluate its performance.
Date of Conference: 21-24 September 2014
Date Added to IEEE Xplore: 20 November 2014
Electronic ISBN:978-1-4799-3694-6

ISSN Information:

Conference Location: Reims, France

References

References is not available for this document.