抗遮挡的鸡群优化粒子滤波目标跟踪方法

曹洁; 王振莹; 李伟

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抗遮挡的鸡群优化粒子滤波目标跟踪方法

更新时间：2026-09-04

- 抗遮挡的鸡群优化粒子滤波目标跟踪方法
- Anti-occlusion particle filter target tracking method based on chicken swarm optimization
- 微电子学与计算机 2019年36卷第6期页码：40-44
- 作者机构：
  
  1. 兰州理工大学计算机与通信学院,甘肃,兰州,730050
  2. 兰州理工大学电气工程与信息工程学院,甘肃,兰州,730050
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61263031甘肃省自然科学基金 1506RJZA105
- DOI：
  中图分类号：
- 纸质出版：2019
- 稿件说明：
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曹洁, 王振莹, 李伟. 抗遮挡的鸡群优化粒子滤波目标跟踪方法[J]. 微电子学与计算机, 2019, 36(6): 40-44.

CAO Jie, WANG Zhen-ying, LI Wei. Anti-occlusion particle filter target tracking method based on chicken swarm optimization[J]. 2019, 36(6): 40-44.
曹洁, 王振莹, 李伟. 抗遮挡的鸡群优化粒子滤波目标跟踪方法[J]. 微电子学与计算机, 2019, 36(6): 40-44. DOI：

CAO Jie, WANG Zhen-ying, LI Wei. Anti-occlusion particle filter target tracking method based on chicken swarm optimization[J]. 2019, 36(6): 40-44. DOI：

摘要

针对遮挡情况下粒子滤波算法因粒子贫化而导致目标跟踪精度下降的问题

提出一种抗遮挡的鸡群优化粒子滤波跟踪方法.首先将粒子权值作为适应度来确定粒子的种类及关系

通过不同类型粒子的移动机制来完成位置更新; 然后引入线性递减权重策略和向种群中最优个体学习部分来克服鸡群优化局部最优问题

并选择似然函数值最大的区域作为目标; 最后

采用模板更新来继续遮挡情况下的跟踪.理论分析和实验结果表明

该算法相比于基本鸡群优化粒子滤波和粒子滤波跟踪方法

对遮挡目标具有良好的鲁棒性且跟踪精度较高.

Abstract

In order to solve the poor target tracking accuracy problem caused by particle impoverishment in particle filter (PF) algorithm under the occlusion condition

an Anti-occlusion Chicken Swarm Optimization-based particle filter target tracking method is proposed. Firstly

the weight of particles is as the fitness and the type of each particle in the population and interrelation among particles is determined

and various movement mechanism of different types of particles are introduced to update the position. Then individual learning for food are adjusted through linear decrementing weight strategy and guided by the optimal individual of the population to overcome local optimal problems

and we define the area which has the biggest likelihood function value as the target. Finally

the tracking is continued by the updating templates under the occlusion condition. Theoretical analysis and experimental results show that the proposed algorithm has better robustness and higher tracking accuracy for occlusion targets than the chicken swarm optimization-based particle filter (CSO-PF) and the particle filter tracking methods.

关键词

Keywords

references

WU Y, LIM J, YANG M H. Object Tracking Benchmark[J]. IEEE Transactions on Pattern Analysis % Machine Intelligence, 2015, 37(9):1834-1848. DOI: 1834

ZHAO J, LI Z. Particle filter based on Particle Swarm Optimization resampling for vision tracking[J]. Expert Systems with Applications, 2010, 37(12):8910-8914. DOI: 8910

TANG B. A modified PSO based particle filter algorithm for object tracking[C]// Spie Defense, Security, % Sensing. Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering XI, 2013: 87500S.

MENG X, LIU Y, GAO X, et al. A New Bio-inspired Algorithm: Chicken Swarm Optimization[M]// Advances in Swarm Intelligence. Springer International Publishing, 2014: 86-94.

吴定会, 孔飞, 纪志成.鸡群算法的收敛性分析[J].中南大学学报(自然科学版), 2017(8):2105-2112. DOI: 2105

杨伟明, 赵美蓉, 黄银国, 等.基于空间分布和时间序列分析的粒子滤波算法[J].电子学报, 2017, 45(2):300-306. DOI: 300

张建春, 康凤举, 梁洪涛, 等.基于鸡群优化的粒子滤波算法研究[J].系统仿真学报, 2017, 29(2):295-300. DOI: 295

曹洁, 李伟, 李军, 等.强噪声背景下鲁棒的说话人跟踪[J].华中科技大学学报(自然科学版), 2015(增刊1):363-366. DOI: 363

侯志强, 韩崇昭.视觉跟踪技术综述[J].自动化学报, 2006, 32(4):603-617. DOI: 603

周卫东, 孙天, 储敏, 等.交互式多模型粒子滤波优化重采样算法[J].北京航空航天大学学报, 2017, 43(5):865-871. DOI: 865

王宇霞, 赵清杰, 蔡艺明, 等.基于自重构粒子滤波算法的目标跟踪[J].计算机学报, 2016, 39(7):1294-1306. DOI: 1294

IBARGUREN A, MAURTUA I, PERRZ M A, et al. Multiple target tracking based on particle filtering for safety in industrial robotic cells[J]. Robotics % Autonomous Systems, 2015, 72(C):105-113. DOI: 105

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