Research on improved tracking algorithm for complex environmental problems

LU Yan-jun; WANG Shi-yu; ZHANG Tai-ning; ZHAO Wei-ping

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Research on improved tracking algorithm for complex environmental problems

更新时间：2026-09-04

- Research on improved tracking algorithm for complex environmental problems
- Vol. 37, Issue 9, Pages: 78-82(2020)
- 作者机构：
  
  沈阳航空航天大学机构自动化学院,辽宁,沈阳,110136
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2020
- 稿件说明：
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卢艳军, 王诗宇, 张太宁, 赵为平. 针对复杂环境问题的改进跟踪算法研究[J]. 微电子学与计算机, 2020, 37(9): 78-82.

LU Yan-jun, WANG Shi-yu, ZHANG Tai-ning, et al. Research on improved tracking algorithm for complex environmental problems[J]. 2020, 37(9): 78-82.
卢艳军, 王诗宇, 张太宁, 赵为平. 针对复杂环境问题的改进跟踪算法研究[J]. 微电子学与计算机, 2020, 37(9): 78-82. DOI：

LU Yan-jun, WANG Shi-yu, ZHANG Tai-ning, et al. Research on improved tracking algorithm for complex environmental problems[J]. 2020, 37(9): 78-82. DOI：

摘要

为解决无人机跟踪目标时面对的复杂环境问题，选择以基于颜色特征进行跟踪的MeanShift算法为基础，对其作出改进.针对无人机跟踪目标时，目标颜色与环境背景相似的问题，提出改进MeanShift算法的跟踪特征，将相机采集的视频格式由RGB颜色空间转化为HSV颜色空间，并选取其中的色调分量作为跟踪特征；针对无人机跟踪过程中目标被短期遮挡的问题，提出将改进后的MeanShift算法与Kalman滤波相结合，利用Kalman滤波的预测功能，使算法在面对目标被短期遮挡的情况下依旧能稳定跟踪.通过实验，验证了本文提出的跟踪算法的有效性.

Abstract

In order to solve the complex environmental problems faced by the drone tracking target

the MeanShift algorithm based on color feature tracking is selected and improved. Aiming at the problem that the target color is similar to the environment background when the target is tracked by the drone

the tracking feature of the MeanShift algorithm is proposed. The video format captured by the camera is converted from the RGB color space to the HSV color space

and the hue component is selected as the tracking feature. For the problem that the target is short-term occlusion during the tracking process of the UAV

the improved MeanShift algorithm is combined with Kalman filtering

and the prediction function of Kalman filtering is used to make the algorithm stable in the face of short-term occlusion. The effectiveness of the tracking algorithm proposed in this paper is verified by experiments.

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references

KALMAN R E. A New Approach to Linear Filtering and Prediction Problems[J]. Journal of Basic Engineering, 1960, 82(1):35-37. DOI: 35

FUKUNAGA K, HOATETLER L. The estimation of the gradient of a density function, with applications in pattern recognition[J]. IEEE Transactions on Information Theory, 1975, 21(1):32-40. DOI: 32

GAO W. Research on Target Tracking Technology of Airborne Optoelectronic Platform[D]. Changchun: Changchun Institute of Optoelectronics, Chinese Academy of Sciences, 2012.

HILL B, ROGER T, VORHAGEN F W. Comparative analysis of the quantization of color spaces on the basis of the CIELAB color-difference formula[J]. ACM Transactions on Graphics, 1997, 16(2):109-154. DOI: 109

HE Q. Research on Moving Target Tracking Method Based on Image Sequence[D]. Chongqing: Chongqing University, 2007.

LU X J. Research on moving target detection and tracking algorithm[D]. Suzhou: Suzhou University, 2011.

DONG M F. Research on target tracking algorithm based on image saliency detection[D]. Changchun: Jilin University, 2015.

CHENG Y. Mean shift, mode seeking, and clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 17(8):790-799. DOI: 790

LIEN S F, HSIA K H, SU J P. Moving Target Tracking based on CamShift Approach and Kalman Filter[J]. Applied Mathematics % Information Sciences, 2015, 9(1):395-401. DOI: 395

HE Y J, LI G X. Face detection tracking and feature point positioning system[J]. Electronic Design Engineering, 2012(8):189-192. DOI: 189

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