Non-Bayesian Track-Before-Detect Using Cauchy-Schwarz Divergence-Based Information Fusion

被引：0

作者：

Gostar, Amirali K. ^{[1
]}

Rathnayake, Tharindu ^{[1
]}

Tennakoon, Ruwan ^{[1
]}

Bab-Haidashar, Alireza ^{[1
]}

Hoseinnezhad, Reza ^{[1
]}

机构：

[1] RMIT Univ, Sch Engn, Bundoora, Vic 3083, Australia

来源：

2018 21ST INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION) | 2018年

基金：

澳大利亚研究理事会;

关键词：

Finite set statistics; track-before-detect; random set theory; Poisson random finite set; cell tracking;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper we present a novel non-Bayesian filtering method for tracking multiple objects with a particular application in time-lapse cell microscopic video sequence. In our method the heat-map of the frame sequence is extracted and represented as a pseudo-probability hypothesis density of the image. The pseudo-probability hypothesis density is used as measurements and fused with a prior Poisson random finite set density. We employed Cauchy-Schwarz divergence for information fusion. The presented algorithm was tested on a publicly available cell microscopic video sequence.

引用

页码：289 / 294

页数：6

共 32 条

[1] Cauchy-Schwarz Divergence-Based Distributed Fusion with Poisson Random Finite Sets
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