A Survey on Multi-source Person Re-identification
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摘要: 行人重识别是近年来计算机视觉领域的热点问题, 经过多年的发展, 基于可见光图像的一般行人重识别技术已经趋近成熟. 然而, 目前的研究多基于一个相对理想的假设, 即行人图像都是在光照充足的条件下拍摄的高分辨率图像. 因此虽然大多数的研究都能取得较为满意的效果, 但在实际环境中并不适用. 多源数据行人重识别即利用多种行人信息进行行人匹配的问题. 除了需要解决一般行人重识别所面临的问题外, 多源数据行人重识别技术还需要解决不同类型行人信息与一般行人图片相互匹配时的差异问题, 如低分辨率图像、红外图像、深度图像、文本信息和素描图像等. 因此, 与一般行人重识别方法相比, 多源数据行人重识别研究更具实用性, 同时也更具有挑战性. 本文首先介绍了一般行人重识别的发展现状和所面临的问题, 然后比较了多源数据行人重识别与一般行人重识别的区别, 并根据不同数据类型总结了5 类多源数据行人重识别问题, 分别从方法、数据集两个方面对现有工作做了归纳和分析. 与一般行人重识别技术相比, 多源数据行人重识别的优点是可以充分利用各类数据学习跨模态和类型的特征转换. 最后, 本文讨论了多源数据行人重识别未来的发展.Abstract: Person re-identification (Re-ID) has been a popular and well-investigated topic in computer vision community. However, current researches have a relatively ideal assumption that person images are captured under a sufficient light condition and with high-resolution. Although most researches can achieve very exciting performances, they are not suitable for practical applications. Since practical conditions are a little complicated, and there are multiple sources to represent persons' appearance. In this paper, we focus on the multi-source person Re-ID, which refers to the problem of using multiple sources of data for person re-identification. Compared with general person Re-ID methods, multi-source person Re-ID researches are more practical, yet more challenging in reality. We need to face challenges caused by domain gap among different data sources, such as low-resolution images, infrared images, depth images, text information and sketch images. In this paper, we start with a brief introduction of general person Re-ID. The differences between general and multi-source person Re-ID are then compared. Five types of multi-source person Re-ID are further analyzed and summarized. From these discussions, it will become evident that several advantages exist in multi-source person Re-ID over general person Re-ID methods, as the former can make full use of data sources to learn cross-modality feature transformation. Finally, the future trends of multi-source person Re-ID are discussed.
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图 2 多源数据行人重识别类型
Fig. 2 Scope of multi-source data person re-identification studied in this survey
图 3 一般行人重识别与多源数据行人重识别论文数量和最优效果对比
Fig. 3 The state-of-the-art performance and number of papers between general Re-ID and multi-source data Re-ID
表 1 一般行人重识别与多源数据行人重识别的对比
Table 1 Comparison of general Re-ID and multi-source data Re-ID
一般行人重识别 多源数据行人重识别 定义 给定一个监控行人图像, 检索跨设备下的该行人图像的技术 给定一个监控行人的跨类型或模态信息/图像, 检索跨设备跨模态下的该行人图像的技术 数据类型 单一类型的图像 多类型的图像/视频、文本、语言、素描等数据信息 方法 针对输入图像提取稳定、鲁棒且能描述和区分不同行人的特征信息, 计算特征相似性, 根据相似性大小排序 使用特定于类型/域的网络提取该类型/域的特征信息, 通过共享网络生成特征, 使用合适的损失函数进行训练并与普通网络相连确保重识别工作的有效性 数据集 单一的可见光图像、二分类属性数据集 多种图像、多种信息、多属性数据集 解决重点和难点 低分辨率、视角和姿势变化、光照变化、遮挡和视觉模糊性问题 模态变化以及一般行人重识别需要克服的问题 
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表 2 多源数据行人重识别工作中的代表性方法
Table 2 A summary of representational methods in multi-source data Re-ID
方法 模态 年份 会议/期刊 方法类别 数据集 度量学习 特征模型 统一模态 JUDEA[7] 高−低分辨率图像 2015 ICCV 度量学习 ⑩⑪⑫ √ × × SLD2L[9] 2015 CVPR 字典学习 ⑪⑬⑭ × √ × SALR-REID[8] 2016 IJCAI 子空间学习 ⑩⑮⑯ √ √ × SING[14] 2018 AAAI 超分辨率 ⑰⑱⑲ × √ √ CSR-GAN[15] 2018 IJCAI 超分辨率 ⑩⑮⑯ × √ √ DSPDL[11] 2018 AAAI 字典学习 ⑪⑭⑳ × √ × Zhuang[18] 2018 CVPR 深度对偶学习 ㉑㉒㉓ √ × √ Wu[22] 红外−可见光图像 2017 ICCV 深度零填充 ㉔ × √ × TONE[24] 2018 AAAI 度量学习 ㉕ √ √ × Ye[23] 2018 IJCAI 特征学习 ㉔㉕ √ √ × cmGAN[25] 2018 IJCAI 特征嵌入 ㉔ × √ × D2RL[26] 2019 CVPR 图像生成 ㉔㉕ × √ √ Barbosa[27] 深度−可见光图像 2012 ECCV 度量学习 ㉖ √ × × Wu[30] 2017 TIP 子空间学习 ㉖㉗㉘ √ √ × Hafner[31] 2018 CVPR 模态转移 ㉗㉚ × √ √ Ye[40] 文本−可见光图像 2015 ACM 度量学习 ①④㉛ √ × × Shi[35] 2015 CVPR 属性识别 ①⑤㉛ √ × × APR[37] 2017 CVPR 属性识别 ⑦⑧ √ × × GNA-RNN[42] 2017 CVPR 密切关系学习 ㉜ × √ × CNN-LSTM[41] 2017 ICCV 特征学习 ㉜ × √ × MTL-LORAE[39] 2018 PAMI 特征学习 ①③④⑨ √ √ × Pang[45] 素描−可见光图像 2018 ACM MM 特征学习 ㉝ × √ ×
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表 3 常用的一般行人重识别数据集与跨模态行人重识别数据集
Table 3 A summary of general Re-ID dataset and multi-source data Re-ID datase
类别 数据集名称 发布时间 数据集类型 人数 相机数量 数据集大小 一般行人数据集 ①VIPeR[51] 2008 真实数据集 632 2 1 264幅 RGB 图像 ②3DPES[52] 2011 192 8 1 011 幅 RGB 图像 ③i-LIDS[50] 2009 119 2 476 幅 RGB 图像 ④PRID2011[53] 2011 934 2 1 134 幅 RGB 图像 ⑤CUHK01[48] 2012 971 2 3 884幅 RGB 图像 ⑥CUHK03[6] 2014 1 467 10 13 164幅 RGB 图像 ⑦Market-1501[54] 2015 1 501 6 32 217 幅 RGB 图像 ⑧DukeMT MC-REID[55] 2017 1 812 8 36 441 幅 RGB 图像 ⑨SAIVT-SoftBio[56] 2012 152 8 64 472 幅 RGB 图像 低分辨率行人数据集 ⑩CAVIAR[57] 2011 真实数据集 72 2 720 幅高分辨率图像 500 幅低分辨率图像 ⑪LR-VIPeR[7, 9-11] 2015 模拟数据集 632 2 1 264 幅 RGB 图像 ⑫LR-3DPES[7] 2015 192 8 1 011 幅 RGB 图像 ⑬LR-PRID2011[9, 15] 2015 100 2 200 幅 RGB 图像 ⑭LR-i-LDIS[9, 11] 2015 119 2 238 幅 RGB 图像 ⑮SALR-VIPeR[8, 15] 2016 632 2 1 264 幅 RGB 图像 ⑯SALR-PRID[8, 15] 2016 450 2 900 幅 RGB 图像 ⑰MLR-VIPeR[14] 2018 632 2 1 264 幅 RGB 图像 ⑱MLR-SYSU[14] 2018 502 2 3 012 幅 RGB 图像 ⑲MLR-CUHK03[14] 2018 1 467 2 14 000 幅 RGB 图像 ⑳LR-CUHK01[11] 2018 971 2 1 942 幅 RGB 图像 ㉑LR-CUHK03[18] 2018 1 467 10 13 164 幅 RGB 图像 ㉒LR-Market-1501[18] 2018 1 501 6 32 217 幅 RGB 图像 ㉓LR-DukeMTMC-REID[18] 2018 1 812 8 36 441 幅 RGB 图像 红外行人数据集 ㉔SYSU-MM01[22] 2017 真实数据集 491 6 287 628 幅 RGB 图像 15 792幅红外图像 ㉕RegDB[58] 2017 412 2 4 120 幅 RGB 图像 4 120 幅红外图像 深度图像行人数据集 ㉖PAVIS[27] 2012 真实数据集 79 − 316 组视频序列 ㉗BIWI RGBD-ID[28] 2014 50 − 22 038 幅 RGB-D 图像 ㉘IAS-Lab RGBD-ID[28] 2014 11 − 33 个视频序列 ㉙Kinect REID[59] 2016 71 − 483 个视频序列 ㉚RobotPKU RGBD-ID[60] 2017 90 − 16 512 幅 RGB-D 图像 文本行人数据集 ㉛PETA[34] 2014 真实数据集 8 705 − 19 000 幅图像 66 类文字标签 ㉜CUHK-PEDES[42] 2017 13 003 − 40 206 幅图像 80 412 个句子描述 素描行人数据集 ㉝Sketch Re-ID[45] 2018 真实数据集 200 2 400 幅 RGB 图像 200 幅素描
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表 4 几种多源数据行人重识别方法在常用的行人数据集上的识别结果
Table 4 Comparison of state-of-the-art methods on infra-red person re-identification dataset
数据集 算法 年份 Rank1 (%) Rank5 (%) Rank10 (%) 低分辨率 VIPeR SLD2L[9] 2015 16.86 41.22 58.06 MVSLD2L[10] 2017 20.79 45.08 61.24 DSPDL[11] 2018 28.51 61.08 76.11 CAVIAR JUDEA[7] 2015 22.12 59.56 80.48 SLD2L[9] 2015 18.40 44.80 61.20 SING[14] 2018 33.50 72.70 89 红外 SYSU-MM01 Wu等[22] 2017 24.43 − 75.86 Ye等[23] 2018 17.01 − 55.43 CMGAN[25] 2018 37.00 − 80.94 RegDB Ye等[23] 2018 33.47 − 58.42 TONE[24] 2018 16.87 − 34.03 深度图像 BIWI RGBD-ID Wu等[30] 2017 39.38 72.13 − Hafner[31] 2018 36.29 77.77 94.44 PAVIS Wu等[30] 2017 71.74 88.46 − Ren等[63] 2017 76.70 87.50 96.10 素描 SKETCH Re-ID Pang等[45] 2018 34 56.30 72.50 文本 VIPeR Shi等[35] 2015 41.60 71.90 86.20 SSDAL[38] 2016 43.50 71.80 81.50 MTL-LORAE[39] 2018 42.30 42.30 81.6 PRID SSDAL[38] 2016 22.60 48.70 57.80 MTL-LORAE[39] 2018 18 37.40 50.10 Top1 Top10 文本 CUHK-PEDES CNN-LSTM[41] 2017 25.94 60.48 GNA-RNN[42] 2017 19.05 53.64
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