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

作者：Ren, Yayun; Xu, Benlian^*

来源：Mathematical Problems in Engineering, 2014, 2014: 495765.

DOI：10.1155/2014/495765

• MEAN-SHIFT

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 amulti-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.

• 出版日期2014
• 单位常熟理工学院; 中国矿业大学（北京）