Target Tracking Algorithm Based on Kalman Filter and Particle Filter

DU Chao; LIU Wei-ning; LIU Lian

doi:10.3788/YJYXS20112603.0384

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Target Tracking Algorithm Based on Kalman Filter and Particle Filter

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Target Tracking Algorithm Based on Kalman Filter and Particle Filter
- Chinese Journal of Liquid Crystals and Displays Vol. 26, Issue 3, Pages: 384-389(2011)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100049
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20112603.0384
  CLC： TP391.41
- Received：06 January 2011，
  
  Revised：25 February 2011，
  
  Published Online：20 June 2011，
  
  Published：20 June 2011
- 稿件说明：
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杜超, 刘伟宁, 刘恋. 一种基于卡尔曼滤波及粒子滤波的目标跟踪算法[J]. 液晶与显示, 2011,26(3): 384-389

DU Chao, LIU Wei-ning, LIU Lian. Target Tracking Algorithm Based on Kalman Filter and Particle Filter[J]. , 2011,26(3): 384-389
杜超, 刘伟宁, 刘恋. 一种基于卡尔曼滤波及粒子滤波的目标跟踪算法[J]. 液晶与显示, 2011,26(3): 384-389 DOI： 10.3788/YJYXS20112603.0384.

DU Chao, LIU Wei-ning, LIU Lian. Target Tracking Algorithm Based on Kalman Filter and Particle Filter[J]. , 2011,26(3): 384-389 DOI： 10.3788/YJYXS20112603.0384.

摘要

针对卡尔曼跟踪算法在非线性非高斯情况下跟踪结果不再准确

以及粒子滤波跟踪算法计算量大难以满足实时性的缺陷

提出了卡尔曼滤波及粒子滤波相结合的算法。利用卡尔曼滤波进行跟踪得到候选目标并计算目标模型与候选模型的匹配程度

若与目标模型匹配度小于一定阈值

则转换跟踪方式利用粒子滤波进行跟踪来修正卡尔曼滤波结果;同时

采用"模板缓冲区法"对目标模型进行更新以保证跟踪的连续性、稳定性及准确性。实验结果表明

这种跟踪算法既发挥了卡尔曼滤波的实时性又保持了粒子滤波的准确性

有较好的跟踪性能。

Abstract

Aiming at the problem that Kalman filter tracking algorithm is no longer accurate in non-Gauss and non-linear case and the Particle filter tracking algorithm costs huge computation

a improved targets tracking algorithm based on Kalman filter and Particle filter was proposed. Firstly

a candidate object was gotten by Kalman tracking algorithm.Then

the tracking result would be verified by Particle filter algorithm when the match threshold is lower than a certain. The improved algorithm used "template buffer" to updated object template to ensure the tracking process continuity

stability and accuracy.Experimental results show that this approach can maintain the efficiency of Kalman algorithm and the powerful ability of Particle filter algorithm

so it is of advanced property.

关键词

Keywords

references

Welch G,Bishop G.An introduction to the kalman filter .Carolina:University of North Carolina at Chapel Hill,1997.[2] 毛克诚,孙付平.扩展卡尔曼滤波和采样卡尔曼滤波性能比较[J]. 海洋测绘, 2006,26(5):4-6.[3] Gordon N,Salmond D. Novelapproach to nonlinear/non-Gaussian Bayesian state estimation [J].IEE Proceedings-F,1993,140(2):107-113.[4] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for bayesian filtering [J].Statistics and Computing,2000,10(3):197-208.[5] Shan C F, Wei Y C, Tan T N.Real-time hand tracking by combining particle filtering and mean shift //Proc. of the 6th IEEE International Conference on Automatic Face and Gesture Recognition, Seoul, Korea:IEEE Computer Society, 2004: 669-674.[6] 魏坤,赵永强,潘泉,等.基于均值漂移和粒子滤波的红外目标跟踪[J]. 光电子激光, 2008,19(2):213-217.[7] 朱永松,国澄明.基于相关系数的相关跟踪算法研究[J]. 中国图象图形学报, 2004, 9(8): 963-967.

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