New particle filter object tracking algorithm

Shi Wei Gao; Lei Guo; Ning Yang; Liang Chen; Ya Qin Du

New particle filter object tracking algorithm

Shi Wei Gao
, Lei Guo
, Ning Yang
, Liang Chen
, Ya Qin Du

School of Automation

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

To improve the performance of object tracking, a particle filter algorithm was proposed which uses state partition technique and parallel extended kalman filter to construct proposal distribution. This proposal enhances the estimation accuracy compared to traditional filters. At the same time, color model and shape model are adaptively fused in the framework, a new model update scheme is also combined. The experimental results show the availability of the proposed algorithm.

Original language	English
Pages (from-to)	485-489
Number of pages	5
Journal	Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University
Volume	43
Issue number	3
State	Published - Mar 2009

Keywords

Adaptively fusing
Object tracking
Parallel extended Kalman filter
Particle filter
State partition

Cite this

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title = "New particle filter object tracking algorithm",

abstract = "To improve the performance of object tracking, a particle filter algorithm was proposed which uses state partition technique and parallel extended kalman filter to construct proposal distribution. This proposal enhances the estimation accuracy compared to traditional filters. At the same time, color model and shape model are adaptively fused in the framework, a new model update scheme is also combined. The experimental results show the availability of the proposed algorithm.",

keywords = "Adaptively fusing, Object tracking, Parallel extended Kalman filter, Particle filter, State partition",

author = "Gao, \{Shi Wei\} and Lei Guo and Ning Yang and Liang Chen and Du, \{Ya Qin\}",

year = "2009",

month = mar,

language = "英语",

volume = "43",

pages = "485--489",

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T1 - New particle filter object tracking algorithm

AU - Gao, Shi Wei

AU - Guo, Lei

AU - Yang, Ning

AU - Chen, Liang

AU - Du, Ya Qin

PY - 2009/3

Y1 - 2009/3

N2 - To improve the performance of object tracking, a particle filter algorithm was proposed which uses state partition technique and parallel extended kalman filter to construct proposal distribution. This proposal enhances the estimation accuracy compared to traditional filters. At the same time, color model and shape model are adaptively fused in the framework, a new model update scheme is also combined. The experimental results show the availability of the proposed algorithm.

AB - To improve the performance of object tracking, a particle filter algorithm was proposed which uses state partition technique and parallel extended kalman filter to construct proposal distribution. This proposal enhances the estimation accuracy compared to traditional filters. At the same time, color model and shape model are adaptively fused in the framework, a new model update scheme is also combined. The experimental results show the availability of the proposed algorithm.

KW - Adaptively fusing

KW - Object tracking

KW - Parallel extended Kalman filter

KW - Particle filter

KW - State partition

UR - https://www.scopus.com/pages/publications/64849102913

M3 - 文章

AN - SCOPUS:64849102913

SN - 1006-2467

VL - 43

SP - 485

EP - 489

JO - Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

JF - Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University

IS - 3

ER -

New particle filter object tracking algorithm

Abstract

Keywords

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