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基于高置信局部特征的车辆重识别优化算法

窦鑫泽 盛浩 吕凯 刘洋 张洋 吴玉彬 柯韦

窦鑫泽, 盛浩, 吕凯, 等 . 基于高置信局部特征的车辆重识别优化算法[J]. 北京航空航天大学学报, 2020, 46(9): 1650-1659. doi: 10.13700/j.bh.1001-5965.2020.0067
引用本文: 窦鑫泽, 盛浩, 吕凯, 等 . 基于高置信局部特征的车辆重识别优化算法[J]. 北京航空航天大学学报, 2020, 46(9): 1650-1659. doi: 10.13700/j.bh.1001-5965.2020.0067
DOU Xinze, SHENG Hao, LYU Kai, et al. Vehicle re-identification optimization algorithm based on high-confidence local features[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1650-1659. doi: 10.13700/j.bh.1001-5965.2020.0067(in Chinese)
Citation: DOU Xinze, SHENG Hao, LYU Kai, et al. Vehicle re-identification optimization algorithm based on high-confidence local features[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(9): 1650-1659. doi: 10.13700/j.bh.1001-5965.2020.0067(in Chinese)

基于高置信局部特征的车辆重识别优化算法

doi: 10.13700/j.bh.1001-5965.2020.0067
基金项目: 

国家重点研发计划 2018YFB2100500

国家自然科学基金 61861166002

国家自然科学基金 61872025

国家自然科学基金 61635002

澳门特别行政区科学技术发展基金 0001/2018/AFJ

软件开发环境国家重点实验室开放基金 SKLSDE2019ZX-04

详细信息
    作者简介:

    窦鑫泽  男,硕士研究生。主要研究方向:计算机视觉

    盛浩  男,博士,副教授,博士生导师。主要研究方向:计算机视觉、模式识别和机器学习

    吕凯  男,博士研究生。主要研究方向:计算机视觉

    刘洋  男,博士研究生。主要研究方向:计算机视觉

    张洋  男,博士研究生。主要研究方向:计算机视觉

    吴玉彬  男,博士研究生。主要研究方向:计算机视觉

    柯韦  男,博士,副教授。主要研究方向:模式识别

    通讯作者:

    盛浩.E-mail:shenghao@buaa.edu.cn

  • 中图分类号: TP399

Vehicle re-identification optimization algorithm based on high-confidence local features

Funds: 

National Key R & D Program of China 2018YFB2100500

National Natural Science Foundation of China 61861166002

National Natural Science Foundation of China 61872025

National Natural Science Foundation of China 61635002

Science and Technology Development Fund, Macau SAR 0001/2018/AFJ

Open Fund of the State Key Laboratory of Software Development Environment SKLSDE2019ZX-04

More Information
  • 摘要:

    根据车辆重识别中区域置信度不同,提出了基于高置信局部特征的车辆重识别优化算法。首先,利用车辆关键点检测获得对应的多个关键点坐标信息,分割出车标扩散区域和其他重要的局部区域。根据车标扩散区域的高区分度特性,提升局部区域的置信度。使用多层卷积神经网络对输入图片进行处理,根据局部区域分割信息,对卷积得到的特征张量进行空间维度上的切割,获得代表全局信息和关键局部信息的特征张量。然后,通过全连接层特征张量转化为表示车辆个体的一维向量,计算损失函数。最后,在测试阶段使用全局特征,并利用训练好的车标扩散区域提取分支获得高置信局部特征,缩短局部识别一致的车辆目标距离。在典型车辆重识别数据集VehicleID上进行测试,验证了所提算法的有效性。

     

  • 图 1  常见的车标扩散区域

    Figure 1.  Popular vehicle brand extension regions

    图 2  不同车标扩散区域的相似车型

    Figure 2.  Similar vehicle models with different vehicle brand extension regions

    图 3  车辆关键点检测结果

    Figure 3.  Vehicle key points detection results

    图 4  基于高置信局部特征的车辆重识别优化算法网络结构

    Figure 4.  Network structure of vehicle re-identification optimization algorithm based on high-confidence local features

    图 5  本文算法流程

    Figure 5.  Proposed algorithm flowchart

    图 6  车标图片分布

    Figure 6.  Image distribution of vehicle brands

    图 7  验证集上的实验结果

    Figure 7.  Experimental results on validation set

    表  1  VehicleID数据集信息

    Table  1.   VehicleID dataset information

    数据集 图片数量 车辆ID数量
    训练集 110 178 13 164
    small测试集 7 332 800
    medium测试集 12 995 1 600
    large测试集 20 038 2 400
    下载: 导出CSV

    表  2  不同算法top1的识别准确率对比结果

    Table  2.   Top1 recognition accuracy results of different methods

    算法 topl的识别准确率/%
    small
    测试集
    medium
    测试集
    large
    测试集
    VGG+CCL[17] 43.6 37.0 32.9
    OIFE[19] 67.0
    NuFACT[20] 48.9 43.6 38.6
    VAMI[21] 63.1 52.9 47.3
    C2F[22] 61.1 56.2 51.4
    RAM[23] 75.2 72.3 67.7
    Part-Regularized[16] 78.4 75.0 74.2
    本文 79.2 77.1 75.2
    下载: 导出CSV

    表  3  top1的识别准确率消融对比结果

    Table  3.   Ablation comparison results of top1 recognition accuracy

    算法 topl的识别准确率/%
    small
    测试集
    medium
    测试集
    large
    测试集
    仅主分支 78.4 75.9 74.4
    仅高置信局部特征分支 24.1 18.9 15.5
    两个分支(本文算法) 79.2 77.1 75.2
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-03-02
  • 录用日期:  2020-03-20
  • 网络出版日期:  2020-09-20

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