A Target Tracking Algorithm of Smart Munition Based on Multiple Features

NIE Zhen-gang; SUN Xie-chang; LIANG Yin-chuan

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A Target Tracking Algorithm of Smart Munition Based on Multiple Features

更新时间：2026-09-04

- A Target Tracking Algorithm of Smart Munition Based on Multiple Features
- Vol. 33, Issue 5, Pages: 89-91(2016)
- 作者机构：
  
  1. 北京理工大学信息与电子学院,北京,100081
  2. 天地一体化信息技术国家重点实验室(筹),北京,100086
  3. 航天恒星科技有限公司,北京,100086
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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聂振钢, 孙协昌, 梁银川. 基于多特征Mean-Shift的灵巧弹药末制导跟踪算法[J]. 微电子学与计算机, 2016, 33(5): 89-91, 96.

NIE Zhen-gang, SUN Xie-chang, LIANG Yin-chuan. A Target Tracking Algorithm of Smart Munition Based on Multiple Features[J]. 2016, 33(5): 89-91.
聂振钢, 孙协昌, 梁银川. 基于多特征Mean-Shift的灵巧弹药末制导跟踪算法[J]. 微电子学与计算机, 2016, 33(5): 89-91, 96. DOI：

NIE Zhen-gang, SUN Xie-chang, LIANG Yin-chuan. A Target Tracking Algorithm of Smart Munition Based on Multiple Features[J]. 2016, 33(5): 89-91. DOI：

摘要

针对灵巧弹药在复杂背景下跟踪目标的问题

将跟踪目标颜色信息和边缘信息进行融合

提出了一种基于多特征的Mean-Shift跟踪算法

从而解决了复杂背景下跟踪目标的鲁棒性问题.应用色差空间表示跟踪目标的颜色信息

同时引入边缘信息表示跟踪目标的纹理信息

从而可以有效解决复杂背景下Mean Shift跟踪鲁棒性问题.应用色差空间代替色调空间

并用反映射方法求解加权值

从而节省了运算复杂度

满足实时性要求.实验结果表明

改进算法跟踪性能较经典Mean Shift法具有明显提高

满足工程应用需求.

Abstract

For the accurate target tracking of smart munitions under complex background

a improved Mean Shift tracking algorithm based on color features and edge feature is proposed to improve the robustness of the Mean Shift algorithm. Due to a color-difference space is introduced to represent color features and edge information is introduced to represent texture feature

the robustness of Mean Shift algorithm is significantly enhanced. Using color-difference space instead of hue space and using back-projection method to resolve weighted coefficients

the complexity of algorithm is reduced and the real-time demand is meet. Experimental results show that compared with the state-art Mean Shift algorithm

the proposed multiple feature Mean Shift algorithm has improved the tracking performance and suited to engineering implementation. autonomous landing.

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Keywords

references

DComaniciu, P Meer, Mean shift. A robust approach toward feature space analysis[J]. IEEE Trans Patten Analysis and Machine Intelligence, 2002, 23(5): 603-619. DOI: 603

Yizong Cheng. Mean Shift, mode seeking, and clustering[J]. IEEE Trans Patten Analysis and Machine Intelligence, 1995, 17(8): 790-799. DOI: 790

Gary R Bradski. Computer vision face tracking for use in a perceptual user interface[J]. Intel Technology Journal, 1998, 18(3): 231-247. DOI: 231

Jordan J, Angelopoulou E. Mean-shift clustering for interactive multispectral image analysis[C]//proceedings of 2013 IEEE International Conference on Image Processing. Merbourne: IEEE, 2013: 3790-3794.

刘汉忠, 顾晓辉. 灵巧弹药半实物仿真系统及模型[J] 火力与指挥控制, 2005, 30(1): 55-58. DOI: 55

Demitri N, Zoubir A M. A robust kernel density estimator based mean-shiftalgorithm[C]//proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal. Florence: IEEE, 2014: 7964-7968.

Xiao An, Jaedo Kim, Youngjoon Han. Optimal colour-based mean shift algorithm for tracking objects[J]. Computer Vision, 2014, 8(3): 235-244. DOI: 235

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