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基于滤波器自适应更新的机场目标跟踪算法

杨临风 牟睿 黎新 李炜

杨临风, 牟睿, 黎新, 李炜. 基于滤波器自适应更新的机场目标跟踪算法[J]. 交通信息与安全, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009
引用本文: 杨临风, 牟睿, 黎新, 李炜. 基于滤波器自适应更新的机场目标跟踪算法[J]. 交通信息与安全, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009
YANG Linfeng, MOU Rui, LI Xin, LI Wei. Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique[J]. Journal of Transport Information and Safety, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009
Citation: YANG Linfeng, MOU Rui, LI Xin, LI Wei. Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique[J]. Journal of Transport Information and Safety, 2022, 40(1): 72-79. doi: 10.3963/j.jssn.1674-4861.2022.01.009

基于滤波器自适应更新的机场目标跟踪算法

doi: 10.3963/j.jssn.1674-4861.2022.01.009
基金项目: 

国家重点研发计划项目 2018YFC0809503

四川省科技计划重点研发项目 2020YFG0134

详细信息
    作者简介:

    杨临风(1993—),硕士. 研究方向:空中交通管理. E-mail: 1002120943@qq.com

    通讯作者:

    牟睿(1993—),本科,三级飞行员. 研究方向:飞行技术及智慧机场.E-mail: 285921595@qq.com

  • 中图分类号: TP391.41

Development of an Object Tracking Algorithm for Airports Using Adaptive Filter Update Technique

  • 摘要: 机场场面目标跟踪常面临目标遮挡、背景干扰、低分辨率等因素的影响,导致跟踪准确性降低甚至丢失跟踪目标。针对以上问题,研究了基于滤波器自适应更新的机场目标跟踪算法。选取跟踪目标的颜色特征和深度特征,通过插值算子进行多特征融合,再将融合特征与之对应的滤波器进行卷积求和计算各区域置信度,置信度高的区域即为跟踪目标位置。为提高跟踪准确性,利用峰值旁瓣比与平均响应峰值能量建立了跟踪结果校验机制,并设计了1种滤波器自适应更新策略,使滤波器能够自适应调整学习速率,仅在结果可靠时更新。在西南某机场采集的视频数据集上进行测试,结果表明:算法在目标特征不明显或发生变化时具有更好的性能,在目标遮挡和背景干扰等9种因素下的跟踪性能有较大提升,整体精确度和成功率分别达到0.834和0.828,较原ECO算法分别提升了11.35%和11.29%,且均优于文中提到的其他5种经典算法。

     

  • 图  1  ECO算法流程图

    Figure  1.  ECO algorithm flow chart

    图  2  可视化特征图

    Figure  2.  Visualized feature map

    图  3  本文算法流程图

    Figure  3.  Proposed algorithm flow chart

    图  4  遮挡因素下的响应图变化

    Figure  4.  Response map variation in occlusion factor

    图  5  2种校验机制的测试结果

    Figure  5.  Test results of the two verification mechanisms

    图  6  本文算法与其他算法的测试结果

    Figure  6.  Test results of the proposed and other algorithms

    图  7  本文和其他算法的跟踪效果

    Figure  7.  Tracking effect of the proposed and other algorithms

    表  1  跟踪因素表

    Table  1.   Tracking factors table

    序号 跟踪因素
    1 光照变化(illumination variation, IV)
    2 尺度变化(scale variation, SV)
    3 平面外旋转(out-of-plane rotation, OPR)
    4 遮挡(occlusion, OCC)
    5 变形(deformation, DEF)
    6 运动模糊(motionblur, MB)
    7 快速运动(fast motion, FM)
    8 平面内旋转(in-plane rotation, IPR)
    9 出视野(out-of-view, OV)
    10 背景干扰(backgroundclutters, BC)
    11 低分辨率(lowresolution,LR)
    下载: 导出CSV

    表  2  不同跟踪因素下的精确度对比表

    Table  2.   Precision comparison table under different tracking factors

    跟踪因素 ECO 本文算法
    IV 0.626 0.705
    SV 0.733 0.785
    OPR 0.782 0.884
    OCC 0.779 0.833
    DEF 0.703 0.862
    MB 0.620 0.698
    FM 0.694 0.667
    IPR 0.689 0.833
    OV 0.833 0.765
    BC 0.619 0.765
    LR 0.611 0.701
    Overall 0.749 0.834
    下载: 导出CSV

    表  3  不同跟踪因素下的成功率对比表

    Table  3.   Success rates comparison table under different tracking factors

    跟踪因素 ECO 本文算法
    IV 0.602 0.695
    SV 0.757 0.798
    OPR 0.716 0.847
    OCC 0.769 0.823
    DEF 0.724 0.872
    MB 0.643 0.709
    FM 0.673 0.662
    IPR 0.681 0.789
    OV 0.830 0.777
    BC 0.624 0.762
    LR 0.610 0.686
    Overall 0.744 0.828
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-08-11
  • 网络出版日期:  2022-03-31

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