Improved multi-target tracking algorithm based on Gaussian mixture particle PHD filter

被引：0

作者：

Lin, Qing ^{[1
,2
]}

Cao, Pei ^{[1
]}

Liao, Dingan ^{[1
,3
]}

Zhan, Yongzhao ^{[1
]}

Yang, Yaping ^{[1
]}

机构：

[1] School of Computer Science and Telecommunication Engineering, Jiangsu University, Zhenjiang, 212013, Jiangsu

[2] School of Computer Science and Technology, Nanjing University and Technology, Nanjing, 210094, Jiangsu

[3] Changzhou Textile Garment Institute, Changzhou, 213164, Jiangsu

来源：

International Journal of Multimedia and Ubiquitous Engineering | 2015年 / 10卷 / 02期

关键词：

Gaussian Particle Filter; Mixture Particle Filter; Multi-target Tracking; Probability Hypothesis Density;

D O I：

10.14257/ijmue.2015.10.2.21

中图分类号：

学科分类号：

摘要：

The paper proposes Gaussian mixture particle probability hypothesis density filter(PHD) algorithm,which can effectively solve the problem that the object number is changing or unknown, based on particle PHD filter. This algorithm calculates the object number and state by recursive procedure, avoiding the uncertainty of target state estimation caused by particle sampling and clustering. Gaussian mixture particle is introduced to effectively maintain the multi-modal distribution of each target, reducing the complexity of calculation. © 2015 SERSC.

引用

页码：227 / 236

页数：9

共 17 条

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