The Application of Particle Filter Algorithm in Multi-target Tracking

被引：0
|
作者：
Liu, Jiaomin [1 ]
Meng, Junying [1 ]
Wang, Juan [1 ]
Han, Ming [1 ]
机构：
[1] Yanshan Univ, Coll Informat Sci & Engn, Qinhuangdao, Peoples R China
来源：
ADVANCES IN MULTIMEDIA, SOFTWARE ENGINEERING AND COMPUTING, VOL 2 | 2011年 / 129卷
关键词：
Particle filter; Multi-target tracking; Sequential important sampling; MCMC;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Particle filter is a probability estimation method based on Bayesian framework and it has unique advantage to describe the target tracking non-linear and non-Gaussian. In this paper, firstly, analyses the particle degeneracy and sample impoverishment in particle filter multi-target tracking algorithm, and secondly, it applies Markov Chain Monte Carlo (MCMC) method to improve re-sampling process and enhance performance of particle filter algorithm. Finally, the performance of the proposed method is certificated by experiment that tracking multiple targets of similar appearance and complex motion. The results show the efficacy of the proposed method in multi-target tracking.
引用
收藏
页码：419 / 424
页数：6
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