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基于上下文感知和尺度自适应的实时目标跟踪

夏爱明 伍雪冬

夏爱明, 伍雪冬. 基于上下文感知和尺度自适应的实时目标跟踪[J]. 红外技术, 2021, 43(5): 429-436.
引用本文: 夏爱明, 伍雪冬. 基于上下文感知和尺度自适应的实时目标跟踪[J]. 红外技术, 2021, 43(5): 429-436.
XIA Aiming, WU Xuedong. Real-time Object Tracking Based on Context Awareness and Scale Adaptation[J]. Infrared Technology , 2021, 43(5): 429-436.
Citation: XIA Aiming, WU Xuedong. Real-time Object Tracking Based on Context Awareness and Scale Adaptation[J]. Infrared Technology , 2021, 43(5): 429-436.

基于上下文感知和尺度自适应的实时目标跟踪

基金项目: 

国家自然科学基金 61671222

详细信息
    作者简介:

    夏爱明(1985-),男,硕士研究生,主要研究方向为视觉目标跟踪。E-mail: 362931408@qq.com

    通讯作者:

    伍雪冬(1975-),男,博士,教授,主要研究方向为计算机视觉, 能源预测与环境经济调度等。E-mail: woolcn@163.com

  • 中图分类号: TP391.41

Real-time Object Tracking Based on Context Awareness and Scale Adaptation

  • 摘要: 针对传统核相关滤波视觉目标跟踪算法在快速运动、背景杂波、运动模糊等情况下跟踪精度低且不能处理尺度变化的问题,提出了一种基于上下文感知和尺度自适应的实时目标跟踪算法。该算法在核相关滤波算法框架的基础上,引入了上下文感知和尺度自适应方法,增加了背景信息且能够处理目标的尺度变化。首先,利用融合了fHOG(fusion histogram of oriented gradient)、CN(color names)和灰度的特征对目标区域进行采样,训练一个二维位移滤波器,然后,在目标区域建立尺度金字塔,利用fHOG对目标区域进行多尺度采样,训练一个一维尺度滤波器,最后,在模型更新阶段改进了更新策略。在标准数据集OTB-2015上对100组视频序列进行的试验结果表明,提出的算法比基准算法(kernel correlation filter, KCF)精度提高了13.9%,成功率提高了14.2%,且优于实验中对比的其他跟踪算法。在尺度变化、运动模糊、快速运动等条件下,提出的算法在准确跟踪的同时,能够保持较高的速度。
  • 图  1  融合特征的可视化

    Figure  1.  Visualization of the fusion features

    图  2  采样区域

    Figure  2.  Sampling area

    图  3  尺度金字塔

    Figure  3.  Scale pyramid

    图  4  本文算法流程图

    Figure  4.  The flow chart of the proposed algorithm

    图  5  100个视频序列的精度图和成功率图

    Figure  5.  Accuracy diagram and success rate diagram of 100 video sequences

    图  6  不同跟踪环境曲线图

    Figure  6.  The graphs of the different tracking environments

    图  7  四种最优算法可视化效果图

    Figure  7.  Four optimal algorithms visualization

    表  1  6种最新算法的特点

    Table  1.   The features of the 6 latest algorithms

    Algorithm Feature Scale
    CSK gray No
    KCF HOG No
    DSST Displacement: HOG,gray;Scale: HOG Yes
    CN gray, CN No
    ASLA - Yes
    IKCF Displacement: gray, HOG, CN;Scale: HOG Yes
    下载: 导出CSV

    表  2  11种算法运行速度对比

    Table  2.   Comparison of 11 algorithms' running speeds  fps

    Algorithm CarDark Car4 David2 Sylvester Trellis Average speed
    CT 86.93 73.14 54.85 55.39 63.84 66.83
    IVT 15.70 16.65 15.20 16.58 17.18 16.26
    DFT 13.67 4.07 10.69 7.56 6.38 8.47
    ASLA 1.70 1.21 1.56 1.45 1.65 1.51
    L1APG 0.77 0.43 0.56 0.57 0.53 0.57
    ORIA 22.25 8.19 16.61 4.26 5.61 11.38
    CSK 488.46 52.75 482.92 112.83 67.21 240.83
    KCF 331.10 22.44 291.70 91.07 42.26 155.72
    DSST 54.63 5.32 53.38 14.94 6.96 27.05
    CN 155.09 30.38 258.27 81.25 16.42 108.28
    IKCF 39.23 9.47 46.56 24.85 9.24 25.87
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-08
  • 修回日期:  2021-03-19
  • 刊出日期:  2021-05-22

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