Moving Target Fast Tracking Using SURF and Cluster Analysis Method

LI Ying; LI Jing-yu; XU Zheng-ping

doi:10.3788/YJYXS20112604.0544

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Moving Target Fast Tracking Using SURF and Cluster Analysis Method

Imaging Technology & Image Processing | 更新时间：2020-08-12

- Moving Target Fast Tracking Using SURF and Cluster Analysis Method
- Chinese Journal of Liquid Crystals and Displays Vol. 26, Issue 4, Pages: 544-550(2011)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 长春理工大学光电工程学院,吉林长春,130022
  3. 中国科学院研究生院北京,100039
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20112604.0544
  CLC： TP311
- Received：10 March 2011，
  
  Revised：29 March 2011，
  
  Published Online：20 August 2011，
  
  Published：20 August 2011
- 稿件说明：
移动端阅览
李英, 李静宇, 徐正平. 结合SURF与聚类分析方法实现运动目标的快速跟踪[J]. 液晶与显示, 2011,26(4): 544-550

LI Ying, LI Jing-yu, XU Zheng-ping. Moving Target Fast Tracking Using SURF and Cluster Analysis Method[J]. , 2011,26(4): 544-550
李英, 李静宇, 徐正平. 结合SURF与聚类分析方法实现运动目标的快速跟踪[J]. 液晶与显示, 2011,26(4): 544-550 DOI： 10.3788/YJYXS20112604.0544.

LI Ying, LI Jing-yu, XU Zheng-ping. Moving Target Fast Tracking Using SURF and Cluster Analysis Method[J]. , 2011,26(4): 544-550 DOI： 10.3788/YJYXS20112604.0544.

摘要

为了解决运动目标快速跟踪过程的实时性与稳定跟踪问题

提出了结合SURF(Speed Up Robust Features)与K-means聚类分析的运动目标快速跟踪算法(SURF-KMs)

对图像的局部多尺度特征提取与描述进行了研究。首先

使用SURF算法在跟踪窗口内提取特征点

生成并匹配特征矢量。然后

利用K-means算法估计目标特征点的质心位置

确定其聚集范围

实时更新窗口尺寸和位置。最后

建立目标模板更新策略

当目标发生形态变化而无遮挡时

更新目标模板。实验结果表明

当目标发生大角度旋转和快速缩放

同时发生颜色变化时

所提出的SURF-KMs算法仍能够实现稳定的跟踪

且满足运动目标实时跟踪的稳定可靠、精确度高、抗干扰能力强等指标要求。

Abstract

In order to design a moving target fast tracking system with respect to a time-limited and stable tracking process

specially when the shape of moving objective or its environment condition change

a new approach named SURF-KMs which combines the advantage of SURF algorithm with a cluster analysis of K-means method is proposed. First

based on SURF algorithm

interest points and vectors are presented. Second

using K-means method

we can estimate the target-center

determine targets cluster scope and update the tracking window online. Finally

a self-adapting updating strategy for matching template is also proposed in order to track moving target automatically. Experimental results indicate that SURF-KMs is mostly able to achieve a stable tracking while the monitored target rotating

scale changing

and also the environment illumination glittering. Moreover

it can satisfy the system requirements of tracking stability

higher precision and anti-jamming.

关键词

Keywords

references

Lindeberg T. Feature detection with automatic scale selection [J]. International Journal of Computer Vision, 1998, 30(2): 79-116.[2] Lowe D G. Distinctive image features from scale-invariant keypoints [J]. International Journal of Computer Vision(IJCV), 2004, 60(2):91-110.[3] 纪华,吴元昊,孙宏海,等. 结合全局信息的SIFT特征匹配算法[J]. 光学精密工程, 2009,17(2):439-444.[4] 杨晓敏,吴炜,卿粼波,等. 图像特征点提取及匹配技术[J]. 光学精密工程, 2009,17(9):2276-2282.[5] 程明明,王贺,安平,等.基于特征点匹配的多视图像校正[J]. 液晶与显示 ,2010,25(4):593-597.[6] Bay H, Tuytelaars T, Van Gool L. SURF: Speeded up robust features // European Conference on Computer Vision(ECCV), Graz Austria:TUG and Univercity of Ljubljana, 2006, 3951: 404-417.[7] Bay H, Tuytelaars T, Van Gool L.Speeded-up robust features(SURF) [J]. Computer Vision and Image Understanding, 2008, 110(3): 346-359.[8] Neto H V, Nehmzow U. Automated exploration and inspection: Comparing two visual novelty detectors [J]. International Journal of Advanced Robotic Systems,2005,2(4):355-362.[9] Luo J, Oubong G. A comparison of SIFT, PCA-SIFT and SURF [J]. International Journal of Image Processing, 2009,3(4): 143-152.[10] 许丽利. 聚类分析的算法及应用 .长春:吉林大学,2010.[11] 张坤华,杨烜. 应用聚类和分型实现复杂背景下的扩展目标分割[J]. 光学精密工程, 2009,17(7):1665-1671.[12] 王永明,王贵锦. 图像局部不变性特征与描述 [M].北京:国防工业出版社,2010.

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