基于电子稳像跟踪技术的运动滤波算法

王斌; 赵跃进

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基于电子稳像跟踪技术的运动滤波算法

信息科学 | 更新时间：2020-08-12

- 基于电子稳像跟踪技术的运动滤波算法
- Motion filtering algorithm for tracking technology based on image stabilization
- 光学精密工程 2009年17卷第1期页码：202-206
- 作者机构：
  
  北京理工大学信息科学技术学院光电工程系北京,100081
- 作者简介：
- 基金信息：
  
  国防科技预研基金资助项目(1040603)
- DOI：
  中图分类号： TP391
- 收稿日期：2008-04-22，
  
  修回日期：2008-11-18，
  
  纸质出版日期：2009
- 稿件说明：
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王斌, 赵跃进. 基于电子稳像跟踪技术的运动滤波算法[J]. 光学精密工程, 2009,17(1): 202-206

WANG Bin, ZHAO Yue-jin. Motion filtering algorithm for tracking technology based on image stabilization[J]. Editorial Office of Optics and Precision Engineering, 2009,17(1): 202-206
王斌, 赵跃进. 基于电子稳像跟踪技术的运动滤波算法[J]. 光学精密工程, 2009,17(1): 202-206 DOI：

WANG Bin, ZHAO Yue-jin. Motion filtering algorithm for tracking technology based on image stabilization[J]. Editorial Office of Optics and Precision Engineering, 2009,17(1): 202-206 DOI：

摘要

为了在摄像机平台不稳的情况下获取稳定的图像序列

实现基于电子稳像的目标跟踪

对摄像机主动扫描运动与随机抖动分离方法进行了研究

提出了用均值偏移和粒子滤波结合的运动滤波算法(MSPF)来实现运动分离。算法通过粒子滤波预测粒子

然后利用单次均值偏移迭代移动粒子

使粒子更接近于目标真实位置区域

削弱了计算结果精度对粒子数的依赖。对于现实中的复杂背景

利用MSPF算法分离摄像机的主动扫描运动和随机抖动

采用运动补偿图像差分法检测出运动目标

从而实现图像的稳定跟踪。实验结果表明

MSPF算法使用50%的粒子就能起到传统粒子滤波算法同样的效果

缩短了计算时间

有利于实现实时稳像跟踪

适用于车载、船载、机载等稳定跟踪系统中。

Abstract

In order to obtain steady image sequence from unsteady camera and to realize object tracking based on image stabilization

a separating method of active scanning movement from random jitter of camera are presented. Mean Shift with Particle Filter (MSPF) algorithm is proposed to realize motion separation. This algorithm uses particle filter to forecast particles

and then move them to be close to the real position of object through single mean shift iteration

which can undermine the dependence of the calculation accuracy on the number of particles. To a complex background in reality

the MSPF algorithm can be used to separate active scanning movement from random jitter of camera

and then image difference and motion compensation can be used to detect moving object to realize the stability of image tracking. Experimental results show that the MSPF algorithm with 50% particles can achieve the same effect with traditional particle filter

which means proposed algorithm can shortens process time and can realize real-time image stabilization for vehicle-borne

ship-borne

air-borne and other stable tracking systems.

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Keywords

references

赵红颖, 金宏, 熊经武. 电子稳像技术概述[J]. 光学精密工程,2001,9(4):353-359. ZHAO H Y, JIN H, XIONG J W. Overview of the electronic image stabilization technology [J]. Opt. Precision Eng., 2001,9(4):353-359. (in Chinese)[2] 罗诗途, 王艳玲, 张玘,等. 车载图像跟踪系统中电子稳像算法的研究 [J]. 光学精密工程, 2005,13(1):95-103. LUO SH T, WANG Y L, ZHANG Q, et al.. Electronic image stabilization algorithm for image tracking system on vehicle [J]. Opt. Precision Eng., 2005,13(1):95-103. (in Chinese)[3] 赵红颖, 晏磊, 熊经武. 舰船图像序列电子稳定算法的研究 [J]. 光学精密工程, 2003,11(6):602-606. ZHAO H Y, YAN L, XIONG J W. Image composition based on EIS model [J]. Opt. Precision Eng., 2003,11(6):602-606. (in Chinese)[4] 钟平, 于前洋, 金光,等. 机载摄像设备图像稳定方法探讨 [J]. 光电工程, 2002,29(增):73-76. ZHONG P, YU Q Y, JIN G, et al.. Study on image stabilizing methods for airborne image pickup equipment [J]. Opto-Electronic Engineering, 2002,29(Supp.):73-76. (in Chinese)[5] WANG J, JIA Y D. Image stabilization for object tracking [J]. SPIE, 2002,4875:875-880.[6] 孟勃, 朱明. 粒子滤波算法在非线性目标跟踪系统中的应用 [J]. 光学精密工程, 2007,15(9):1421-1426. MENG B, ZHU M. Nonlinear object tracking using particle filter [J]. Opt. Precision Eng., 2007,15(9):1421-1426. (in Chinese)[7] 赵鹏, 沈庭芝, 单宝堂. 一种基于粒子滤波的无人机电视导引系统目标跟踪算法 [J]. 光学精密工程, 2008,16(1):134-140. ZHAO P, SHEN T Z, SHAN B T. An object tracking algorithm for TV guiding system of UAV based on particle filter [J]. Opt. Precision Eng., 2008,16(1):134-140. (in Chinese)[8] CHENG C,ANSARI R. Kernel particle filter for visual tracking [J]. IEEE Transactions on Signal Processing Letters, 2005,12(3):242-245.[9] COMANICIU D,MEER P. Mean shift: a robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(5):603-619.[10] SHAN C F, WEI Y C, TAN T N, et al.. Real time hand tracking by combining particle filtering and mean shift . Proceedings of the 6th International Conference on Automatic Face and Gesture Recognition, 2004:669-674.[11] ZHANG B, TIAN W F, JIN ZH H. Joint tracking algorithm using particle filter and mean shift with target model updating [J]. Chinese Optics Letters, 2006,4(10):569-572.[12] COMANICIU D,RAMESH V,MEER P. Kernel-based object tracking [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2003,25(5):564-577.

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