On multitarget pairwise-Markov models
MetadataShow full item record
© 2015 SPIE. Single-and multi-target tracking are both typically based on strong independence assumptions regarding both the target states and sensor measurements. In particular, both are theoretically based on the hidden Markov chain (HMC) model. That is, the target process is a Markov chain that is observed by an independent observation process. Since HMC assumptions are invalid in many practical applications, the pairwise Markov chain (PMC) model has been proposed as a way to weaken those assumptions. In this paper it is shown that the PMC model can be directly generalized to multitarget problems. Since the resulting tracking filters are computationally intractable, the paper investigates generalizations of the cardinalized probability hypothesis density (CPHD) filter to applications with PMC models.
Showing items related by title, author, creator and subject.
Duong, Thi V. T. (2008)Modeling patterns in temporal data has arisen as an important problem in engineering and science. This has led to the popularity of several dynamic models, in particular the renowned hidden Markov model (HMM) [Rabiner, ...
On conditional random fields: applications, feature selection, parameter estimation and hierarchical modellingTran, The Truyen (2008)There has been a growing interest in stochastic modelling and learning with complex data, whose elements are structured and interdependent. One of the most successful methods to model data dependencies is graphical models, ...
Xia, Jianhong (Cecilia); Zeephongsekul, P. (2009)Tourist movement is a complex process, but it provides very useful information for park managers and tourist operators. This paper aims to establish a sound methodology for modelling the spatial and temporal movement of ...