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基于残差学习的自适应无人机目标跟踪算法

刘芳 孙亚楠 王洪娟 韩笑

刘芳, 孙亚楠, 王洪娟, 等 . 基于残差学习的自适应无人机目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(10): 1874-1882. doi: 10.13700/j.bh.1001-5965.2019.0551
引用本文: 刘芳, 孙亚楠, 王洪娟, 等 . 基于残差学习的自适应无人机目标跟踪算法[J]. 北京航空航天大学学报, 2020, 46(10): 1874-1882. doi: 10.13700/j.bh.1001-5965.2019.0551
LIU Fang, SUN Yanan, WANG Hongjuan, et al. Adaptive UAV target tracking algorithm based on residual learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1874-1882. doi: 10.13700/j.bh.1001-5965.2019.0551(in Chinese)
Citation: LIU Fang, SUN Yanan, WANG Hongjuan, et al. Adaptive UAV target tracking algorithm based on residual learning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(10): 1874-1882. doi: 10.13700/j.bh.1001-5965.2019.0551(in Chinese)

基于残差学习的自适应无人机目标跟踪算法

doi: 10.13700/j.bh.1001-5965.2019.0551
基金项目: 

国家自然科学基金 61171119

详细信息
    作者简介:

    刘芳   女,博士,副教授,硕士生导师。主要研究方向:图像处理、可视导航

    孙亚楠   女,硕士研究生。主要研究方向:计算机视觉、目标跟踪

    通讯作者:

    刘芳. E-mail:liufang@emails.bjut.edu.cn

  • 中图分类号: V279;TP391

Adaptive UAV target tracking algorithm based on residual learning

Funds: 

National Natural Science Foundation of China 61171119

More Information
  • 摘要:

    无人机已被广泛应用于军事和民用领域,目标跟踪技术是无人机应用的关键技术之一。针对无人机视频跟踪过程中目标易发生尺度变化、遮挡等问题,提出一种基于残差学习的自适应无人机目标跟踪算法。首先,结合残差学习和空洞卷积的优点构建深度网络提取目标特征,同时克服网络退化问题;其次,将提取的目标特征信息输入核相关滤波算法,构建定位滤波器确定目标的中心位置;最后,根据目标外观特性的不同进行自适应分块,并计算出目标尺度的伸缩系数。仿真实验结果表明:所提算法能够有效应对尺度变化、遮挡等情况对跟踪性能的影响,在跟踪成功率和精确度上均高于其他对比算法。

     

  • 图 1  残差网络结构

    Figure 1.  Residual block structure

    图 2  空洞卷积示意图

    Figure 2.  Schematic diagram of dilated convolution

    图 3  网络整体结构

    Figure 3.  Overall network structure

    图 4  自适应分块

    Figure 4.  Adaptive block diagram

    图 5  基于残差学习的自适应无人机目标跟踪算法流程

    Figure 5.  Flowchart of adaptive UAV target tracking algorithm based on residual learning

    图 6  部分视频仿真结果

    Figure 6.  Partial results of video simulation

    图 7  中心位置误差曲线

    Figure 7.  Curves of center position errors

    图 8  覆盖率曲线

    Figure 8.  Curves of coverage rate

    图 9  在UAV123数据集上的跟踪精确率和成功率

    Figure 9.  Tracking accuracy and success rate in UAV123 dataset

    图 10  在VisDrone2018数据集上的跟踪精确率和成功率

    Figure 10.  Tracking accuracy and success rate in VisDrone2018 dataset

    图 11  尺度变化场景测试曲线

    Figure 11.  Curves of scale change scene test

    表  1  网络模型性能比较

    Table  1.   Performance comparison of network models

    迭代次数 分类准确率
    DC-ResNet CNN ResNet DilatedNet
    30000 0.835 0.775 0.813 0.790
    35000 0.837 0.778 0.817 0.796
    40000 0.837 0.778 0.820 0.795
    45000 0.836 0.780 0.818 0.802
    50000 0.839 0.782 0.821 0.800
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-22
  • 录用日期:  2020-02-02
  • 网络出版日期:  2020-10-20

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