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引入全局上下文特征模块的DenseNet孪生网络目标跟踪

谭建豪 殷旺 刘力铭 王耀南

谭建豪, 殷旺, 刘力铭, 王耀南. 引入全局上下文特征模块的DenseNet孪生网络目标跟踪[J]. 电子与信息学报, 2021, 43(1): 179-186. doi: 10.11999/JEIT190788
引用本文: 谭建豪, 殷旺, 刘力铭, 王耀南. 引入全局上下文特征模块的DenseNet孪生网络目标跟踪[J]. 电子与信息学报, 2021, 43(1): 179-186. doi: 10.11999/JEIT190788
Jianhao TAN, Wang YIN, Liming LIU, Yaonan WANG. DenseNet-siamese Network with Global Context Feature Module for Object Tracking[J]. Journal of Electronics & Information Technology, 2021, 43(1): 179-186. doi: 10.11999/JEIT190788
Citation: Jianhao TAN, Wang YIN, Liming LIU, Yaonan WANG. DenseNet-siamese Network with Global Context Feature Module for Object Tracking[J]. Journal of Electronics & Information Technology, 2021, 43(1): 179-186. doi: 10.11999/JEIT190788

引入全局上下文特征模块的DenseNet孪生网络目标跟踪

doi: 10.11999/JEIT190788
详细信息
    作者简介:

    谭建豪:男,1962年生,教授,硕士生导师,研究方向为计算机视觉、飞行机器人、模式识别

    殷旺:男,1995年生,硕士生,研究方向为计算机视觉、目标跟踪

    刘力铭:男,1996年生,硕士生,研究方向为计算机视觉、目标跟踪、图像分割

    王耀南:男,1957年生,教授,博士生导师,研究方向为智能控制、模式识别技术等

    通讯作者:

    殷旺 yinwang@hnu.edu.cn

  • 中图分类号: TN911.73; TP391.41

DenseNet-siamese Network with Global Context Feature Module for Object Tracking

  • 摘要:

    近年来,采用孪生网络提取深度特征的方法由于其较好的跟踪精度和速度,成为目标跟踪领域的研究热点之一,但传统的孪生网络并未提取目标较深层特征来保持泛化性能,并且大多数孪生网络只提取局部领域特征,这使得模型对于外观变化是非鲁棒和局部的。针对此,该文提出一种引入全局上下文特征模块的DenseNet孪生网络目标跟踪算法。该文创新性地将DenseNet网络作为孪生网络骨干,采用一种新的密集型特征重用连接网络设计方案,在构建更深层网络的同时减少了层之间的参数量,提高了算法的性能,此外,为应对目标跟踪过程中的外观变化,该文将全局上下文特征模块(GC-Model)嵌入孪生网络分支,提升算法跟踪精度。在VOT2017和OTB50数据集上的实验结果表明,与当前较为主流的算法相比,该文算法在跟踪精度和鲁棒性上有明显优势,在尺度变化、低分辨率、遮挡等情况下具有良好的跟踪效果,且达到实时跟踪要求。

  • 图  1  DenseNet的网络结构

    图  2  两种长距离依赖模型图

    图  3  全局上下文GC-Model模块

    图  4  孪生网络目标跟踪框架图

    图  5  SD-GCNet算法框架

    图  6  本文算法与4种算法的跟踪结果对比

    表  1  网络结构

    层名称模板分支搜索分支输出
    卷积层7×7Conv, stride 27×7Conv, stride 261×61×72
    密集连接11×1Conv ×2 +3×3Conv×21×1Conv ×2+3×3Conv×261×61×144
    过渡层11×1Conv+average pool1×1Conv+average pool30×30×36
    密集连接21×1Conv ×4+3×3Conv×41×1Conv ×4+3×3Conv×430×30×180
    过渡层21×1Conv+average pool1×1Conv+average pool15×15×36
    密集连接31×1Conv ×6+3×3Conv×61×1Conv ×6+3×3Conv×615×15×252
    密集连接33×3Conv×33×3Conv×39×9×128
    GC-Model图3图39×9×128
    下载: 导出CSV

    表  2  在VOT2017数据集上与主流算法的基础模型结果对比

    跟踪算法精确度鲁棒性平均重叠期望
    本文算法0.54420.0900.297
    SiamFC0.50034.0310.188
    SiamVGG0.52520.4530.287
    DCFNet0.46535.2020.183
    SRDCF0.48064.1140.119
    DeepCSRDCF0.48319.0070.293
    Staple0.52444.0190.169
    下载: 导出CSV

    表  3  不同属性下算法的跟踪精度对比

    跟踪算法相机移动目标丢失光照变化运动变化目标遮挡尺度变化
    本文算法0.5610.5620.5430.5540.4610.543
    SiamFC0.5130.5130.5560.5140.4160.474
    SiamVGG0.5420.5310.5380.5400.4420.514
    DCFNet0.4850.4720.5320.4640.3770.450
    SRDCF0.4840.5110.5880.4530.4190.447
    Staple0.5540.5280.53710.5230.4590.492
    下载: 导出CSV

    表  4  不同属性下算法的跟踪鲁棒性对比(数字表示失败次数)

    跟踪算法相机移动目标丢失光照变化运动变化目标遮挡尺度变化
    本文算法29.018.03.016.022.011.0
    SiamFC40.031.05.042.032.025.0
    SiamVGG35.015.02.015.019.011.0
    DCFNet50.034.08.031.024.021.0
    SRDCF76.086.09.049.032.029.0
    Staple62.053.05.027.027.017.0
    下载: 导出CSV

    表  5  OTB50中测试序列与其影响因素

    测试序列帧数影响因素
    Bolt18快速移动、相机移动、尺度变化等
    carDark244~363运动模糊、低分辨率、背景杂波等
    Ironman38平面内旋转、快速运动、光照变化等
    Shaking55光照变化、背景模糊等
    Jogging-253遮挡
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-10-16
  • 修回日期:  2020-11-13
  • 网络出版日期:  2020-11-19
  • 刊出日期:  2021-01-15

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