Research on Video Target Tracking Based on Fusion Algorithm

LIU Shu-li; SHI Jian-fang; YANG Jing

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Research on Video Target Tracking Based on Fusion Algorithm

更新时间：2026-09-04

- Research on Video Target Tracking Based on Fusion Algorithm
- Vol. 34, Issue 6, Pages: 89-93(2017)
- 作者机构：
  
  1. 太原理工大学信息工程学院,山西,太原,030024
  2. 太原理工大学信息化管理与建设中心,山西,太原,030024
- 作者简介：
- 基金信息：
- DOI：
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- Published：2017
- 稿件说明：
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刘姝黎, 史健芳, 杨静. 基于融合算法的视频目标跟踪研究[J]. 微电子学与计算机, 2017, 34(6): 89-93.

LIU Shu-li, SHI Jian-fang, YANG Jing. Research on Video Target Tracking Based on Fusion Algorithm[J]. 2017, 34(6): 89-93.
刘姝黎, 史健芳, 杨静. 基于融合算法的视频目标跟踪研究[J]. 微电子学与计算机, 2017, 34(6): 89-93. DOI：

LIU Shu-li, SHI Jian-fang, YANG Jing. Research on Video Target Tracking Based on Fusion Algorithm[J]. 2017, 34(6): 89-93. DOI：

摘要

提出了一种嵌入型融合算法

针对Mean Shift算法缺乏必要的模板更新、窗口宽度固定的不足

利用形态学对目标边缘进行检测

粒子滤波对目标进行预测

实现Mean Shift算法模板实时更新以及核函数宽度自适应.仿真结果表明

在目标背景复杂多变、遮挡等情况下

基于本文算法的视频目标跟踪具有较高的鲁棒性和精确度

且实时性较好.

Abstract

This paper proposes a fusion algorithm for video target tracking. Mean shift algorithm lacks necessary template update and cant change the width of the window. Particle filter algorithm has a good prediction of the occlusion region. Morphological algorithm has an advantage in edge detection. The fusion of the three algorithms can complement each other. The scenario can update the template in real time

and be updated to the width of the kernel function. Simulation results show that the proposed scheme can improve the robustness

real-time and accuracy of target tracking

and solve the problem of video target tracking

such as complex background

occlusion

tracking delay and so on.

关键词

Keywords

references

Chen X, Zhang M, Kai R, et al. Improved mean shift target tracking based on self-organizing maps[J]. Signal Image % Video Processing, 2014, 8(Supp. 1): 103-112. DOI: 103

赵晓敏. 改进Mean Shift跟踪算法的研究与实现[D]. 沈阳: 东北大学, 2010.

Deng Y, Liu F, Chen J, et al. Mean shift tracker with chaotic artificial bee colony and space variant resolution[J]. Optik - International Journal for Light and Electron Optics, 2014, 125(16): 4572-4577. DOI: 4572

张继, 王洪元. 基于局部粒子密度的粒子滤波跟踪方法[J]. 微电子学与计算机, 2012, 29(3): 93-97. DOI: 93

张楠, 刘承志, 范存波, 等. 基于粒子滤波与局部搜索的视频目标跟踪[J]. 微电子学与计算机, 2013, 30(9): 1-4. DOI: 1

徐眯, 徐成鸿, 舒军. 基于Mean shift和粒子滤波的目标跟踪方法[J]. 湖北第二师范学院学报, 2016, 33(2): 22-26. DOI: 22

亢朋朋, 朱锡芳, 南东亮, 等. 一种改进Mean shift算法的视频监控系统[J]. 微电子学与计算机, 2013, 30(2): 153-156. DOI: 153

邢浪漫, 范蟠果. 基于mean shift和粒子滤波的混合目标跟踪算法[J]. 微型机与应用, 2011, 30(20): 47-49. DOI: 47

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