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Mathematical Problems in Engineering
Volume 2014, Article ID 495765, 17 pages
Research Article

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

1School of Electrical & Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
2School of Information & Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

Received 15 July 2013; Accepted 6 December 2013; Published 22 January 2014

Academic Editor: Jian Li

Copyright © 2014 Yayun Ren and Benlian Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.