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    Multi-object particle filter revisited

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kim, D.Y. and Vo, B.T. and Vo, B. 2017. Multi-object particle filter revisited, in Proceedings of the International Conference on Control, Automation and Information Sciences (ICCAIS), Oct 27-29 2016, pp. 42-47. Ansan, Korea: IEEE.
    Source Title
    2016 International Conference on Control, Automation and Information Sciences, ICCAIS 2016
    DOI
    10.1109/ICCAIS.2016.7822433
    ISBN
    9781509006502
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/50663
    Collection
    • Curtin Research Publications
    Abstract

    Instead of the filtering density, we are interested in the entire posterior density that describes the random set of object trajectories. So far only Markov Chain Monte Carlo (MCMC) technique have been proposed to approximate the posterior distribution of the set of trajectories. Using labeled random finite set we show how the classical multi-object particle filter (a direct generalisation of the standard particle filter to the multi-object case) can be used to recursively compute posterior distribution of the set of trajectories. The result is a generic Bayesian multi-object tracker that does not require re-computing the posterior at every time step nor running a long Markov chain, and is much more efficient than the MCMC approximations.

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