适于机载环境对地目标跟踪的粒子滤波设计

宋策; 张葆; 尹传历

doi:10.3788/OPE.20142204.1037

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适于机载环境对地目标跟踪的粒子滤波设计

信息科学 | 更新时间：2020-08-13

- 适于机载环境对地目标跟踪的粒子滤波设计
- Particle filter design for tracking ground targets in airborne environment
- 光学精密工程 2014年22卷第4期页码：1037-1047
- 作者机构：
  
  1. 中国科学院大学北京,中国,100049
  2. 中国科学院长春光学精密机械与物理研究所中国科学院航空光学成像与测量重点实验室,吉林长春,130033
- 作者简介：
- 基金信息：
  
  国家863高技术研究发展计划资助项目（No.2008AA121803）()
- DOI：10.3788/OPE.20142204.1037
  中图分类号： V556;TP391
- 收稿日期：2013-06-27，
  
  修回日期：2013-07-14，
  
  纸质出版日期：2014-04-25
- 稿件说明：
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宋策, 张葆, 尹传历. 适于机载环境对地目标跟踪的粒子滤波设计[J]. 光学精密工程, 2014,22(4): 1037-1047

SONG Ce, ZHANG Bao, YIN Chuan-li. Particle filter design for tracking ground targets in airborne environment[J]. Editorial Office of Optics and Precision Engineering, 2014,22(4): 1037-1047
宋策, 张葆, 尹传历. 适于机载环境对地目标跟踪的粒子滤波设计[J]. 光学精密工程, 2014,22(4): 1037-1047 DOI： 10.3788/OPE.20142204.1037.

SONG Ce, ZHANG Bao, YIN Chuan-li. Particle filter design for tracking ground targets in airborne environment[J]. Editorial Office of Optics and Precision Engineering, 2014,22(4): 1037-1047 DOI： 10.3788/OPE.20142204.1037.

摘要

为提高机载环境对地面强机动性目标跟踪的鲁棒性，本文以粒子滤波为跟踪框架，研究了它的动态模型与观测模型。针对机载环境的特点与跟踪目标的强机动性，提出了基于Kristan双步动态模型结构的加速度双步动态模型（TSA）。根据Yilmaz等人提出的非对称核函数思想，针对实际工程中目标变化特点与实时性要求，提出利用Snake算法提取目标轮廓，以轮廓信息构造非对称核函数的方法。最后，依据上述方法提出了TSA-AK粒子滤波跟踪算法。利用提出的算法对机载环境对地目标跟踪的视频进行了测试，结果表明，本文算法可实现对大幅度变速运动目标的稳定跟踪，正确跟踪率为98%；对大小为25 pixel×30 pixel的目标的处理帧率为26 frame/s。

Abstract

To improve the robustness while tracking a ground target with strong mobility in the cabin environment

a particle filter was taken as tracking framework

and its dynamic model and observation model were investigated. According to the two-stage dynamic model proposed by Kristan

et al

a two-stage acceleration (TSA) dynamic model was proposed for the characteristics of cabin environment and the strong mobility of the tracking target. According to the idea of asymmetric kernel function proposed by Yilmaz

et al

a method was proposed by using Snake algorithm to extract the object contour and to construct an asymmetric kernel function based on contour information to solve the real-time moving target problem. Finally

the TSA-AK particle filter tracking algorithm was proposed based on above methods. The proposed algorithm was tested on the video tracking ground target in cabin environment. The results show that the proposed algorithm can stably track target moving in a wide range of velocity. The targeting accuracy is 98%

and the computing frame rate is 26 frame/s when the object scale is 25 pixel×30 pixel.

关键词

Keywords

references

COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using mean shift[C].Proc. IEEE Conference on Computer Vision and Pattern Recognition, 2000, 2: 142-149.

丘文涛, 赵建. 结合区域分割的SIFT图像匹配方法[J]. 液晶与显示, 2012, 27(6):827-831. QIU W T, ZHAO J. Image matching algorithm combining SIFT with region segmentation[J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(6):827-831. (in Chinese)

吴君钦, 刘昊, 罗勇. 静态背景下的运动目标检测算法[J]. 液晶与显示, 2012, 27(5):682-686. WU J Q, LIU H, LUO Y. Algorithm of moving object detection in static background[J]. Chinese Journal of Liquid Crystals and Displays, 2012, 27(5):682-686. (in Chinese)

BLAKE A, ISARD M. Active Contours[M]. New York: Springer, 1998.

KHAN Z H, GU I Y H, BACKHOUSE A G. Robust visual object tracking using multi-mode anisotropic mean shift and particle filters[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(1): 74-87.

OKUMA K, TALEGHANI A, FREITAS N D, et al. A boosted particle filter: Multitarget detection and tracking[C]. Proc. Eur.Conf. Comput. Vis, 2004, 3021:28-39.

ARULAMPALAM M, MASKELL S, GORDON N, et al. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking[J]. IEEE Trans. Signal Process, 2002, 50(2):174-188.

孟勃, 朱明. MSMC跟踪算法在目标跟踪中的应用[J]. 光学精密工程, 2008, 16(1):122-127. MENG B, ZHU M. Application of MSMC algorithm to visual tracking[J]. Opt. Precision Eng., 2008, 16 (1): 122-127. (in Chinese)

LI X R, JILKOV V P. Survey of maneuvering target tracking: Dynamic models[J]. IEEE Trans. Aerosp. Electron. Syst., 2003, 39(10): 1333-1363.

ZHAI Y, YEARY M B, CHENG S, et al. An object-tracking algorithm based on multiple-model particle filtering with state partitioning[J]. IEEE Transactions on Instrumentation and Measurement. 2009, 58(5): 1797-1809.

SVENSSON D, SVENSSON L. A new multiple model filter with switch time conditions[J]. IEEE Transactions on Signal Processing, 2010, 58(1): 11-25.

CHEN J X, KIM M Y, WANG Y, et al.. Switching Gaussian process dynamic models for simultaneous composite motion tracking and recognition[C]. CVPR, 2009: 2655-2662.

陈爱华, 孟勃, 朱明, 等. 多模式融合的目标跟踪算法[J]. 光学精密工程, 2009, 17(1):185-190. CHEN A H, MENG B, ZHU M, et al. Multi-pattern fusion algorithm for target tracking[J]. Opt. Precision Eng., 2009, 17(1): 185-190. (in Chinese)

KRISTAN M, STANISLAV K, LEONARDIS A. A two-stage dynamic model for visual tracking[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics, 2010, 40(9): 1505-1519.

YILMAZ A. Object tracking by asymmetric Kernel mean shift with automatic scale and orientation selection[C]. CVPR, 2007: 1-6.

ZHOU H, KUMAR K S P. A "current" statistical model and adaptive algorithm for estimating maneuvering targets[J]. Journal of Guidance Control and Dynamics, 1984, 7(5):596-602.

KASS M, WITKIN A, TERZOPOULOUS D. Snake: active contour models[J]. International Journal of Computer Vision, 1988, 1(4):321-331.

WILLIAMS D J, SHAB M. A fast algorithm for active contours and curvature estimation[J]. CVGIP:Image Understanding, 1992, 55(1): 14-16.

COHEN L D. On active contour models and balloons[J]. CVGIP:Image Understanding, 1991, 53(2): 211-218.

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