基于主成分分析的粒子滤波器目标跟踪方法

J4 ›› 2012, Vol. 50 ›› Issue (06): 1156-1162.

基于主成分分析的粒子滤波器目标跟踪方法

王欣^{1, 2} , |赵连义¹, 薛龙¹

1. |吉林大学计算机科学与技术学院, 长春 130012|2. |吉林大学符号计算与知识工程教育部重点实验室, 长春 130012

收稿日期:2012-05-16 出版日期:2012-11-26 发布日期:2012-11-26
通讯作者: 王欣 E-mail:w_x@jlu.edu.cn

Particle Filter Algorithm Based on Principal Component Analysis

WANG Xin^1,2, ZHAO Lian\|yi¹, |XUE Long¹

1. College of Computer Science and Technology, Jilin University, Changchun 130012, China|2. Key Laboratory ofSymbolic Computation and Knowledge Engineer of Ministry of Education, Jilin University, Changchun 130012, China

Received:2012-05-16 Online:2012-11-26 Published:2012-11-26
Contact: WANG Xin E-mail:w_x@jlu.edu.cn

摘要/Abstract

摘要：

提出一种基于主成分分析的粒子滤波器目标跟踪新方法．该方法将主成分分析法和传统的粒子滤波方法相结合, 避免了传统粒子滤波器的过度重采样, 提高了目标跟踪精度．实验结果表明, 该方法对单个目标跟踪精度高, 且对多障碍物下的目标跟踪精度也较高, 适用于复杂背景下的人脸跟踪．与传统粒子滤波方法相比, 该方法提高了目标跟踪的精度和鲁棒性, 避免了粒子退化和粒子贫化．

关键词: 目标跟踪；粒子滤波算法；主成分分析法；重采样

Abstract:

A new principal component analysis particle filter algorithm proposed in this paper combines principal component analysis method with the traditional particle filtermethod. The proposed method avoids undue resampling and improves the accuracy of object tracking. Experimental results demonstrate that this algorithmcan acquire better precision in single object tracking and multiple\|obstacle object tracking, and this algorithm could track human face accurately in complex backgrounds. Compared with present particle filter algorithm, this algorithm performs better in robustness and accuracy and avoids particledegeneration and particle impoverishment.

Key words: object tracking, particle filter algorithm, principal component analysis, resampling

中图分类号:

TP317.4

王欣, 赵连义, 薛龙. 基于主成分分析的粒子滤波器目标跟踪方法[J]. J4, 2012, 50(06): 1156-1162.

WANG Xin, DIAO Lian-Xi, XUE Long. Particle Filter Algorithm Based on Principal Component Analysis[J]. J4, 2012, 50(06): 1156-1162.