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基于卷积注意力模块和无锚框检测网络的行人跟踪算法

张红颖 贺鹏艺

张红颖, 贺鹏艺. 基于卷积注意力模块和无锚框检测网络的行人跟踪算法[J]. 电子与信息学报, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634
引用本文: 张红颖, 贺鹏艺. 基于卷积注意力模块和无锚框检测网络的行人跟踪算法[J]. 电子与信息学报, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634
ZHANG Hongying, HE Pengyi. Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634
Citation: ZHANG Hongying, HE Pengyi. Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network[J]. Journal of Electronics & Information Technology, 2022, 44(9): 3299-3307. doi: 10.11999/JEIT210634

基于卷积注意力模块和无锚框检测网络的行人跟踪算法

doi: 10.11999/JEIT210634
基金项目: 国家重点研发计划(2018YFB1601200),天津市研究生科研创新项目(2020YJSZXS14),四川省青年科技创新研究团队专项计划(2019JDTD0001)
详细信息
    作者简介:

    张红颖:女,博士,教授,硕士生导师,研究方向为图像工程与计算机视觉

    贺鹏艺:男,硕士生,研究方向为图像处理、计算机视觉

    通讯作者:

    张红颖 carole_zhang0716@163.com

  • 中图分类号: TN911.73; TP391.4

Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network

Funds: The National Key R&D Program of China(2018YFB1601200), Tianjin Graduate Scientific Research Innovation Project (2020YJSZXS14), The Special Plan for Sichuan Youth Scientific and Technological Innovation Research Team (2019JDTD0001)
  • 摘要: 针对多目标跟踪过程中遮挡严重时的目标身份切换、跟踪轨迹中断等问题,该文提出一种基于卷积注意力模块 (CBAM)和无锚框(anchor-free)检测网络的行人跟踪算法。首先,在高分辨率特征提取网络HrnetV2的基础上,对stem阶段引入注意力机制,以提取更具表达力的特征,从而加强对重识别分支的训练;其次,为了提高算法的运算速度,使检测和重识别分支共享特征权重且并行运行,同时减少头网络的卷积通道数以降低参数运算量;最后,设定合适的参数对网络进行充分的训练,并使用多个测试集对算法进行测试。实验结果表明,该文算法相较于FairMOT在2DMOT15, MOT17, MOT20数据集上的精确度分别提升1.1%, 1.1%, 0.2%,速度分别提升0.82, 0.88, 0.41 fps;相较于其他几种主流算法拥有最少的目标身份切换次数。该文算法能够更好地适用于遮挡严重的场景,实时性也有所提高。
  • 图  1  FairMOT在MOT16-03上可视化效果图及对应的中心点热图

    图  2  框架结构

    图  3  本文网络结构

    图  4  CBAM添加策略

    图  5  特征可视化效果图

    图  6  head结构图

    图  7  ADL-Rundle-8跟踪结果

    图  8  PETS09-S2L1跟踪结果

    图  9  ETH-Pedcross2跟踪结果

    表  1  本文网络部分权重参数

    权重
    conv164×3×3×3
    caca.fc1(4×64×1×1) ca.fc2(64×4×1×1)
    sa1×2×7×7
    conv264×64×3×3
    Layer1[(64×64×1×1),(64×64×3×3),(64×256×1×1)]
    [(256×64×1×1),(64×64×3×3),(64×256×1×1)]×3
    ca1ca1.fc1(16×256×1×1) ca1.fc2(256×16×1×1)
    sa11×2×7×7
    ······
    last layer64×270×3×3,bias=64
    hmhm.0(64×64×3×3,bias=64) hm.2(1×64×1×1,bias=1)
    whwh.0(64×64×3×3,bias=64) wh.2(2×64×1×1,bias=2)
    idid.0(64×64×3×3,bias=64)id.2(128×64×1×1,bias=128)
    regreg.0(64×64×3×3,bias=64) reg.2(2×64×1×1,bias=2)
    下载: 导出CSV

    表  2  不同CBAM添加策略下的检测性能对比(%)

    骨干网络IDF1IDPIDR
    Hrnetv2-w1874.681.169.1
    HrnetV2-w18(stem)+CBAM(a)75.388.664.0
    HrnetV2-w18(stem)+CBAM(b)73.877.170.8
    HrnetV2-w18(stem)+CBAM(c)76.678.874.4
    下载: 导出CSV

    表  3  不同网络的计算量和参数量对比

    网络Total flops(GMac)Total params(MB)Head flops(GMac)Head params(MB)
    HrnetV2-w1870.4410.2025.8840.625
    本文51.099.746.4750.156
    下载: 导出CSV

    表  4  测评指标及其解释说明

    测评指标指标解释
    FP↓被误认为是正样本的比率,即误检率
    FN↓被误认为是负样本的比率,即漏检率
    IDS↓目标ID切换次数,即目标身份发生变化次数
    MOTA↑跟踪准确度。综合FP, FN, IDS等指标计算而来
    MOTP↑定位精度。检测响应与真实数据的行人框重合率
    FPS↑跟踪速度。每秒处理的帧数,用于衡量模型的实时性
    下载: 导出CSV

    表  5  本文算法与FairMOT的测试结果

    数据集算法MOTA↑MOTP↑IDS↓FN↓FP↓fps↑
    2DMOT15FairMOT71.778.61366100184918.31
    本文72.878.61194619301819.13
    MOT20FairMOT12.877.8442210982616243414.69
    本文13.077.2433111059075328815.10
    MOT17FairMOT75.181.12238550922644216.23
    本文76.284.587969141999617.11
    下载: 导出CSV

    表  6  本文算法与其他几种模型及算法的测试结果对比

    数据集跟踪算法MOTA↑MOTP↑IDS↓FN↓FP↓Time elapsed(s)
    MOT17_trainTube_TK[15]79.588.435705685086015316.88
    CSTrack[16]75.981.4196258947201781009.53
    TransCenter[17]70.184.8201794979380215948.00
    Fair(DLA-34)[5]76.380.61620429246366971.25
    Fair(HrnetV2-w18)[5]75.181.122385509226442982.79
    本文76.284.5879691419996932.24
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-28
  • 修回日期:  2021-09-14
  • 网络出版日期:  2021-09-28
  • 刊出日期:  2022-09-19

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