WU Hao, ZHANG Yong, LI Xin, SI Minghua, WANG Weiming. High-precision Template Matching Tracking Algorithm for Optoelectronic Tracking System[J]. Infrared Technology , 2022, 44(12): 1301-1308.
Citation: WU Hao, ZHANG Yong, LI Xin, SI Minghua, WANG Weiming. High-precision Template Matching Tracking Algorithm for Optoelectronic Tracking System[J]. Infrared Technology , 2022, 44(12): 1301-1308.

High-precision Template Matching Tracking Algorithm for Optoelectronic Tracking System

More Information
  • Received Date: June 19, 2018
  • Revised Date: November 17, 2022
  • To achieve high-precision measurements under the operating conditions of optoelectronic tracking systems and satisfy high-precision target matching in complex environments, in this study we adopted the average normalized cross-correlation algorithm. To improve the matching speed and real-time tracking, the computational complexity was simplified by using the sum table method to correlate the sum of images, squares, and the correlation of images. The wavelet pyramid method was used as the search strategy, and the center of the template was used as the reference point for cross-shaped search. A termination threshold was introduced, which reduced the number of mismatched points to increase the search speed. To verify the effectiveness of the algorithm, an optoelectronic tracking system was placed on a two-dimensional turntable in an experiment that used the algorithm to track a target. The experimental results show that the missed target was controlled within 3 pixels. The proposed algorithm can realize high-precision and stable tracking in optoelectronic tracking systems.
  • [1]
    朱泓谕. 模板匹配技术在图像识别中的运用[J]. 电子技术与软件工程, 2021(5): 122-123. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU202105062.htm

    ZHU Hongyu. Application of template matching technique in image recognition[J]. Electronic Technology & Software Engineering, 2021(5): 122-123. https://www.cnki.com.cn/Article/CJFDTOTAL-DZRU202105062.htm
    [2]
    Lewis J P. Fast normalized cross correlation[C]//Proceeding of Vision Interface, 1995: 120-123.
    [3]
    陈翔, 陈鹏. 基于改进模板匹配的目标跟踪算法[J]. 计算机应用, 2011, 31(z2): 127-128. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2011S2040.htm

    CHEN Xiang, CHEN Peng. Object tracking algorithm based on improved template matching[J]. Journal of Computer Applications, 2011, 31(z2): 127-128. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY2011S2040.htm
    [4]
    谢维达, 周宇恒, 寇若岚. 一种改进的快速归一化互相关算法[J]. 同济大学学报(自然科学版), 2011, 39(8): 1233-1237. DOI: 10.3969/j.issn.0253-374x.2011.08.025

    XIE Weida, ZHOU Yuheng, KOU Ruolan. An improved fast normalized cross correlation algorithm[J]. Journal of Tongji University (Natural Science), 2011, 39(8): 1233-1237. DOI: 10.3969/j.issn.0253-374x.2011.08.025
    [5]
    王斌, 何中市, 伍星, 等. 基于高斯金字塔的图像运动估计算法[J]. 计算机工程与应用, 2015, 51(7): 174-178. DOI: 10.3778/j.issn.1002-8331.1305-0110

    WANG Bin, HE Zhongshi, WU Xing, et al. Image motion estimation algorithm based on Gaussian pyramid[J]. Computer Engineering and Applications, 2015, 51(7): 174-178. DOI: 10.3778/j.issn.1002-8331.1305-0110
    [6]
    胡敏, 贺晓佳, 王晓华. 快速区域质心图像匹配算法[J]. 电子测量与仪器学报, 2011, 25(5): 455-462. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201105013.htm

    HU Min, HE Xiaojia, WANG Xiaohua. Fast image matching algorithm with area centroid[J]. Journal of Electronic Measurement and Instrumentation, 2011, 25(5): 455-462. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201105013.htm
    [7]
    YANG Zhuo. Fast template matching based on normalized cross correlation with centroid bounding[C]//International Conference on Measuring Technology and Mechatronics Automation of IEEE, 2010: 224-227.
    [8]
    穆欣侃, 罗海波. 一种对亮度变化鲁棒的相关跟踪方法[J]. 红外与激光工程, 2012, 40(1): 255-260. DOI: 10.3969/j.issn.1007-2276.2012.01.048

    MU Xinkan, LUO Haibo. Correlation tracking approach robust to the variation of image brightness[J]. Infrared and Laser Engineering, 2012, 40(1): 255-260. DOI: 10.3969/j.issn.1007-2276.2012.01.048
    [9]
    徐一鸣, 顾菊平, 袁媛, 等. 基于改进归一化积相关算法的目标跟踪方法研究[J]. 南通大学学报(自然科学版), 2013, 12(2): 11-15. DOI: 10.3969/j.issn.1673-2340.2013.02.003

    XU Yiming, GU Juping, YUAN Yuan, et al. Research on target tracking method based on an improved normalized product correlation algorithm[J]. Journal of Nantong University(Natural Science Edition), 2013, 12(2): 11-15. DOI: 10.3969/j.issn.1673-2340.2013.02.003
    [10]
    WEI Shouder, LAI Shanghong. Fast template matching based on normalized cross correlation with adaptive multilevel winner update[J]. IEEE Transactions on Image Processing, 2008, 17(11): 2227-2235. DOI: 10.1109/TIP.2008.2004615
    [11]
    Tsai D M, LIN C T. Fast normalized cross correlation for defect detection[J]. Pattern Recognition Letters, 2003, 24: 2625-2631. DOI: 10.1016/S0167-8655(03)00106-5
    [12]
    Gharavi-Alkhansari M. A fast globally optimal algorithm for template matching using low-resolution pruning[J]. IEEE Transactions on Image Processing, 2001, 10(4): 526-533. DOI: 10.1109/83.913587
    [13]
    吴强, 任琳, 张杰. 快速归一化互相关算法及DSP优化实现[J]. 电子测量与仪器学报, 2011, 25(6): 495-499. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201106002.htm

    WU Qiang, REN Lin, ZHANG Jie. Fast algorithm of normalized cross correlation and optimized implementation on DSP[J]. Journal of Electronic Measurement and Instrument, 2011, 25(6): 495-499. https://www.cnki.com.cn/Article/CJFDTOTAL-DZIY201106002.htm
    [14]
    陈岳军, 孙广玲, 姚恒. 结合小波金字塔的空频域亚像素图像配准[J]. 上海大学学报(自然科学版), 2012, 18(4): 342-348. DOI: 10.3969/j.issn.1007-2861.2012.04.003

    CHEN Yuejun, SUN Guangling, YAO Heng. Hybrid spatial-frequency domain sub-pixel image registration with wavelet pyramid[J]. Journal of Shanghai University(Natural Science Edition), 2012, 18(4): 342-348. DOI: 10.3969/j.issn.1007-2861.2012.04.003
    [15]
    吴鹏, 徐洪玲, 宋文龙. 结合小波金字塔的快速NCC图像匹配算法[J]. 哈尔滨工程大学学报, 2017, 38(5): 791-796. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201705022.htm

    WU Peng, XU Hongling, SONG Wenlong. A fast NCC image matching algorithm based on wavelet pyramid search strategy[J]. Journal of Harbin Engineering University, 2017, 38(5): 791-796. https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201705022.htm
    [16]
    刘元琳, 宋春凤, 王玲玲. 基于金字塔的渐进分辨率匹配算法研究[J]. 电子制作, 2020(20): 27-29. https://www.cnki.com.cn/Article/CJFDTOTAL-DZZZ202020012.htm

    LIU Yuanlin, SONG Chunfeng, WANG Lingling. Research on progressive resolution matching algorithm based on pyramid[J]. Electronic Production, 2020(20): 27-29. https://www.cnki.com.cn/Article/CJFDTOTAL-DZZZ202020012.htm
    [17]
    张万绪, 吴佳丽, 赵丽平, 等. 改进的十字菱形搜索算法INCDS[J]. 西北大学学报(自然科学版), 2011, 41(2): 226-230. https://www.cnki.com.cn/Article/CJFDTOTAL-XBDZ201102009.htm

    ZHANG Wanxu, WU Jiali, ZHAO Liping, et al. Improved new cross-diamond search algorithm INCDS[J]. Journal of Northwest University(Natural Science Edition), 2011, 41(2): 226-230. https://www.cnki.com.cn/Article/CJFDTOTAL-XBDZ201102009.htm
  • Cited by

    Periodical cited type(18)

    1. 刘传洋,吴一全. 基于红外图像的电力设备识别及发热故障诊断方法研究进展. 中国电机工程学报. 2025(06): 2171-2196 .
    2. 刘垚. 复合绝缘子表面异常发热点识别方法. 光通信研究. 2024(06): 42-47 .
    3. 戴月明,杨陆锋,童雄敏. 基于红外图像与深度学习的EMS低压设备实时断面状态校核方法. 红外技术. 2024(12): 1464-1470 . 本站查看
    4. 刘宸轩. 基于小波分析的电气设备故障检测方法研究. 专用汽车. 2023(01): 58-60 .
    5. 张宗包. 基于图像融合技术的变电站二次设备热故障自主定位研究. 机械与电子. 2023(03): 18-22 .
    6. 魏岸若. 基于数据驱动的电气自动化设备运行故障预警研究. 电气技术与经济. 2023(06): 71-74+89 .
    7. 林航,方宁. 模糊聚类算法在舰船电气系统故障远程检测中的应用. 舰船科学技术. 2022(04): 156-160 .
    8. 常硕,梁杰,姜久超. 基于模拟退火算法的水电站电气装置故障运行状态自动捕捉方法. 水利水电技术(中英文). 2022(03): 110-118 .
    9. 杨琳玮. 基于物联网技术的电气设备全生命周期风险预测方法. 自动化技术与应用. 2022(06): 73-76 .
    10. 葛黄徐,郑雷,江洪,郭一凡,周东国. 基于MST框架的PCNN输电线路红外热故障区域提取方法. 红外技术. 2022(07): 709-715 . 本站查看
    11. 邵健. 基于无线传感网络的建筑电气设备自动监测系统. 能源与环保. 2022(08): 251-256 .
    12. 刘俊,章磊. 基于半导体激光干涉技术的电气设备状态检测研究. 激光杂志. 2021(05): 52-56 .
    13. 王子默,王清亮. 基于梯度扩散的电气设备调试故障行波检测. 计算机仿真. 2020(05): 468-472 .
    14. 邹梓秀. 一种基于MeanShift改进的移动目标跟踪算法. 九江学院学报(自然科学版). 2020(01): 45-47 .
    15. 张平,李东民,苏晓宇,李冠群,朱光南. SIFT特征点匹配算法在GIS设备内部发热判断中的应用. 自动化与仪器仪表. 2020(07): 190-193+197 .
    16. 许晓路,周文,周东国,朱诗沁,倪辉,罗传仙. 基于PCNN分层聚类迭代的故障区域自动提取方法. 红外技术. 2020(08): 809-814 . 本站查看
    17. 王琦,张永杰,周竞,刘庸奇. 基于无线红外热成像仪的变电设备识别和检测. 微型电脑应用. 2020(09): 170-172+176 .
    18. 徐小冰,袁婧,廖雁群,韦亦龙,周承科,周文俊. 基于Faster RCNN与Mean-Shift的电缆附件缺陷红外图像自动诊断方法. 高电压技术. 2020(09): 3070-3080 .

    Other cited types(6)

Catalog

    Article views PDF downloads Cited by(24)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return