A Quantitative Analysis on Two RFS-Based Filtering Methods for Multicell Tracking

Yayun Ren and Benlian Xu

Mathematical Problems in Engineering, 2014, vol. 2014, 1-17

Abstract:

Multiobject filters developed from the theory of random finite sets (RFS) have recently become well-known methods for solving multiobject tracking problem. In this paper, we present two RFS-based filtering methods, Gaussian mixture probability hypothesis density (GM-PHD) filter and multi-Bernoulli filter, to quantitatively analyze their performance on tracking multiple cells in a series of low-contrast image sequences. The GM-PHD filter, under linear Gaussian assumptions on the cell dynamics and birth process, applies the PHD recursion to propagate the posterior intensity in an analytic form, while the multi-Bernoulli filter estimates the multitarget posterior density through propagating the parameters of a multi-Bernoulli RFS that approximates the posterior density of multitarget RFS. Numerous performance comparisons between the two RFS-based methods are carried out on two real cell images sequences and demonstrate that both yield satisfactory results that are in good agreement with manual tracking method.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:495765

DOI: 10.1155/2014/495765

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