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基于多特征子空间与核学习的行人再识别

齐美彬 檀胜顺 王运侠 刘皓 蒋建国

齐美彬, 檀胜顺, 王运侠, 刘皓, 蒋建国. 基于多特征子空间与核学习的行人再识别. 自动化学报, 2016, 42(2): 299-308. doi: 10.16383/j.aas.2016.c150344
引用本文: 齐美彬, 檀胜顺, 王运侠, 刘皓, 蒋建国. 基于多特征子空间与核学习的行人再识别. 自动化学报, 2016, 42(2): 299-308. doi: 10.16383/j.aas.2016.c150344
QI Mei-Bin, TAN Sheng-Shun, WANG Yun-Xia, LIU Hao, JIANG Jian-Guo. Multi-feature Subspace and Kernel Learning for Person Re-identification. ACTA AUTOMATICA SINICA, 2016, 42(2): 299-308. doi: 10.16383/j.aas.2016.c150344
Citation: QI Mei-Bin, TAN Sheng-Shun, WANG Yun-Xia, LIU Hao, JIANG Jian-Guo. Multi-feature Subspace and Kernel Learning for Person Re-identification. ACTA AUTOMATICA SINICA, 2016, 42(2): 299-308. doi: 10.16383/j.aas.2016.c150344

基于多特征子空间与核学习的行人再识别

doi: 10.16383/j.aas.2016.c150344
基金项目: 

国家自然科学基金 61371155

安徽省科技攻关项目 1301b042023

详细信息
    作者简介:

    齐美彬  合肥工业大学计算机与信息学院教授.主要研究方向为视频编码, 运动目标检测与跟踪和DSP技术.E-mail:qimeibin@163.com

    王运侠  合肥工业大学计算机与信息学院硕士研究生.主要研究方向为计算机视觉和图像检索.E-mail:wangyunxia0807@163.com

    刘皓  合肥工业大学计算机与信息学院博士研究生.2014年获得合肥工业大学硕士学位.主要研究方向为图像检索和行人再识别.E-mail:hfut.haoliu@gmail.com

    蒋建国  合肥工业大学计算机与信息学院教授.主要研究方向为数字图像分析与处理, 分布式智能系统和DSP技术及应用.E-mail:jgjiang@hfut.edu.cn

    通讯作者:

    檀胜顺  合肥工业大学计算机与信息学院硕士研究生.主要研究方向为计算机视觉, 图像处理, 行人再识别.本文通信作者.E-mail:tss901118@mail.hfut.edu.cn

Multi-feature Subspace and Kernel Learning for Person Re-identification

Funds: 

National Natural Science Foundation of China 61371155

Science and Technology Brainstorm Project of Anhui Province 1301b042023

More Information
    Author Bio:

    Professor at the School of Computer and Information, Hefei University of Technology. His research interest covers video coding, moving target detection and tracking, and DSP technology

    Master student at the School of Computer and Information, Hefei University of Technology. Her research interest covers computer vision and image retrieval

    Ph. D. candidate at the School of Computer and Information, Hefei University of Technology. He received his master degree from Hefei University of Technology in 2014. His research interest covers image retrieval and person re-identification

    Professor at the School of Computer and Information, Hefei University of Technology. His research interest covers digital image analysis and processing, distributed intelligent systems, DSP technology and applications

    Corresponding author: TAN Sheng-Shun Master student at the School of Computer and Information, Hefei University of Technology. His research interest covers computer vision, image processing, and person re-identification. Corresponding author of this paper.
  • 摘要: 行人再识别指的是在无重叠视域多摄像机监控系统中, 匹配不同摄像机视域中的行人目标.针对当前基于距离测度学习的行人再识别算法中存在着特征提取复杂、训练过程复杂和识别效果差的问题, 我们提出一种基于多特征子空间与核学习的行人再识别算法.该算法首先在不同特征子空间中基于核学习的方法得到不同特征子空间中的测度矩阵以及相应的相似度函数, 然后通过比较不同特征子空间中的相似度之和来对行人进行识别.实验结果表明, 本文提出的算法具有较高的识别率, 其中在VIPeR数据集上, RANK1达到了40.7%, 且对光照变化、行人姿态变化、视角变化和遮挡都具有很好的鲁棒性.
  • 图  1  行人图像分成6个无重叠的水平条带

    Fig.  1  non-overlapping horizontal bands divided by pedestrians image

    图  2  每列为来自不同摄像头场景的同一个人

    Fig.  2  Each column describes the same person captured by different cameras

    图  3  算法在VIPeR数据集上在不同权值 $a$ 下的性能比较

    Fig.  3  Performance comparison at different weights $a$ on the VIPeR dataset

    图  4  算法在iLIDS数据集上在不同权值 $a$ 下的性能比较

    Fig.  4  Performance comparison at different weights $a$ on the iLIDS dataset

    图  5  算法在ETHZ数据集上在不同权值 $a$ 下的性能比较

    Fig.  5  Performance comparison at different weights $a$ on the ETHZ dataset

    图  6  算法在CUHK01数据集上在不同权值 $a$ 下的性能比较

    Fig.  6  Performance comparison at different weights $a$ on the CUHK01 dataset

    表  1  本文算法基于不同核函数在VIPeR数据集上的识别率 (%)

    Table  1  Mathing rates (%) of the proposed algorithm based on different kernel functions on the VIPeR dataset

    Kernel Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    linear 25.1 53.4 67.3 80.1
    $\chi ^{2}$ 38.2 70.0 82.5 91.3
    RBF- $\chi ^{2}$ 40.7 72.37 83.95 92.08
    下载: 导出CSV

    表  2  不同算法在VIPeR数据集上的识别率 (%)

    Table  2  Mathing rates (%) of different methods on the VIPeR dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    PCCA[14] 19.6 51.5 68.2 82.9
    LFDA[27] 19.7 46.7 62.1 77.0
    SVMML[28] 27.0 60.9 75.4 87.3
    KISSME[13] 23.8 54.8 71.0 85.3
    文献[12] 29.7 59.8 73.0 84.1
    rPCCA[20] 22.0 54.8 71.0 85.3
    kLFDA[20] 32.3 65.8 79.7 90.9
    MFA[20] 32.2 66.0 79.7 90.6
    RDC[29] 15.66 38.42 53.86 70.09
    eSDC_knn[10] 26.31 46.61 58.86 72.77
    eSDC_ocsvm[10] 26.74 50.70 62.37 76.36
    Ours 40.7 72.37 83.95 92.08
    下载: 导出CSV

    表  3  不同算法在VIPeR数据集上的识别率 (%)

    Table  3  Mathing rates (%) of different methods on the VIPeR dataset

    Methods Features Rank1 Rank10 Rank20
    SDALF[3] HSV, structures 20 49 56
    PS[5] HSV, structures 22 57 71
    RDC[29] HSV, YCbCr, texture 16 54 70
    KISSME[13] HSV, Lab, LBP 20 62 77
    ITML[30] HSV 15 50 66
    Euclidean HSV 7 23 34
    NRDV[25] HSV 25 65 78
    KRMCA[31] HSV 23.2 72.2 85.8
    Ours HSV 28.4 74.1 86.9
    下载: 导出CSV

    表  4  当 $P=432$ , 不同算法在VIPeR数据集上的识别率 (%)

    Table  4  Mathing rates (%) of different methods at $P=432$ on the VIPeR dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    Euclidean 4.8 11.5 16.4 23.2
    KISSME[13] 17.6 42.6 56.6 71.5
    PRDC[11] 12.6 32 44.3 60
    ITML[30] 8.4 24.5 36.8 52.3
    LMNN[32] 5.1 13.1 20.3 33.9
    文献[12] 22.5 48.6 61.4 74.4
    Ours 28.7 59.3 72.7 83.1
    下载: 导出CSV

    表  5  当 $P=532$ , 不同算法在VIPeR数据集上的识别率 (%)

    Table  5  Mathing rates (%) of different methods at $P=532$ on the VIPeR dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    Euclidean 4.0 10.3 14.8 20.9
    KISSME[13] 11.3 29.4 42.1 56.2
    PRDC[11] 9.1 24.2 34.4 48.6
    ITML[30] 4.2 11.1 17.2 24.6
    LMNN[32] 4.0 9.7 14.2 21.2
    文献[12] 12.4 31.1 43.0 56.7
    Ours 15.5 36.6 49.2 62.1
    下载: 导出CSV

    表  6  不同算法在iLIDS数据集上的识别率 (%)

    Table  6  Mathing rates (%) of different methods on the iLIDS dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    KISSME[13] 28.0 54.2 67.9 81.6
    PCCA[14] 24.1 53.3 69.2 84.8
    LFDA[27] 32.2 56.0 68.7 81.6
    SVMML[28] 20.8 49.1 65.4 81.7
    rPCCA[20] 28.0 56.5 71.8 85.9
    kLFDA[20] 36.9 65.3 78.3 89.4
    MFA[20] 32.1 58.8 72.2 85.9
    ours 38.3 66.5 79.0 88.3
    下载: 导出CSV

    表  7  不同算法在ETHZ数据集上的识别率 (%)

    Table  7  Mathing rates (%) of different methods on the ETHZ dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    KISSME[13] 48.6 65.2 76.4 87.8
    PCCA[14] 40.2 64.4 76.1 88.5
    LFDA[27] 52.8 68.3 78.1 90.8
    SVMML[28] 37.5 65.8 77.6 90.6
    rPCCA[20] 45.5 65.6 76.3 90.1
    kLFDA[20] 53.5 73.3 82.6 91.5
    MFA[20] 52.6 70.2 79.3 90.1
    Ours 61.09 74.77 81.96 91.76
    下载: 导出CSV

    表  8  不同算法在CUHK01数据集上的识别率 (%)

    Table  8  Mathing rates (%) of different methods on the CUHK01 dataset

    Methods Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    KISSME[13] 12.5 31.5 42.5 54.9
    PCCA[14] 17.8 42.4 55.9 69.1
    LFDA[27] 13.3 31.1 42.2 54.3
    SVMML[28] 18.0 42.3 55.4 68.8
    rPCCA[20] 21.6 47.4 59.8 72.6
    kLFDA[20] 29.1 55.2 66.4 77.3
    MFA[20] 29.6 55.8 66.4 77.3
    MidLevel[26] 34.30 55.74 64.52 74.97
    Ours 36.10 62.68 72.61 81.90
    下载: 导出CSV

    表  9  特征核映射前后在VIPeR实验集上的对比效果

    Table  9  Performance comparison between before and after the kernel map on the VIPeR dataset

    Kernel Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    Before 25.66 53.43 67.4 80.5
    After 40.7 72.37 83.95 92.08
    下载: 导出CSV

    表  10  特征核映射前后在iLIDS实验集上的对比效果

    Table  10  Performance comparison between before and after the kernel map on the iLIDS dataset

    Kernel Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    Before 30.4 56.4 67.4 78.3
    After 38.3 66.5 79.0 88.3
    下载: 导出CSV

    表  11  特征核映射前后在ETHZ实验集上的对比效果

    Table  11  Performance comparison between before and after the kernel map on the ETHZ dataset

    Kernel Rank1 (%) Rank5 (%) Rank10 (%) Rank20 (%)
    Before 57.0 72.97 81.5 90.4
    After 61.09 74.77 81.96 91.76
    下载: 导出CSV

    表  12  特征核映射前后在CUHK01实验集上的对比效果

    Table  12  Performance comparison between before and after the kernel map on the CUHK01 dataset

    Kernel Rank1 (%) Rank5 (%)Rank10 (%) Rank20 (%)
    Before 17.92 38.10 48.0 58.84
    After 36.10 62.68 72.61 81.90
    下载: 导出CSV
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