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                    多源數據行人重識別研究綜述

                    葉鈺 王正 梁超 韓鎮 陳軍 胡瑞敏

                    葉鈺, 王正, 梁超, 韓鎮, 陳軍, 胡瑞敏. 多源數據行人重識別研究綜述. 自動化學報, 2020, 46(9): 1869?1884 doi: 10.16383/j.aas.c190278
                    引用本文: 葉鈺, 王正, 梁超, 韓鎮, 陳軍, 胡瑞敏. 多源數據行人重識別研究綜述. 自動化學報, 2020, 46(9): 1869?1884 doi: 10.16383/j.aas.c190278
                    Ye Yu, Wang Zheng, Liang Chao, Han Zhen, Chen Jun, Hu Rui-Min. A survey on multi-source person re-identification. Acta Automatica Sinica, 2020, 46(9): 1869?1884 doi: 10.16383/j.aas.c190278
                    Citation: Ye Yu, Wang Zheng, Liang Chao, Han Zhen, Chen Jun, Hu Rui-Min. A survey on multi-source person re-identification. Acta Automatica Sinica, 2020, 46(9): 1869?1884 doi: 10.16383/j.aas.c190278

                    多源數據行人重識別研究綜述

                    doi: 10.16383/j.aas.c190278
                    基金項目: 國家重點研發計劃(2017YFC0803700), 國家自然科學基金青年項目(61801335, 61876135), 湖北省自然科學基金群體項目(2018CFA024, 2019CFB472, 2018AAA062)資助
                    詳細信息
                      作者簡介:

                      葉鈺:武漢大學計算機學院國家多媒體軟件工程技術研究中心博士研究生. 主要研究方向為圖像處理, 計算機視覺. E-mail: ms.yeyu@whu.edu.cn

                      王正:日本國立信息學研究所學術振興會外國人特別研究員. 2017年獲得武漢大學計算機學院國家多媒體軟件工程技術研究中心博士學位. 主要研究方向為行人重識別和實例搜索. 本文通信作者.E-mail: wangz@nii.ac.jp

                      梁超:武漢大學副教授. 2012年獲得中國科學院自動化研究所博士學位. 主要研究方向為多媒體內容分析和檢索, 計算機視覺和模式識別. E-mail: cliang@whu.edu.cn

                      韓鎮:武漢大學副教授. 2009年獲得武漢大學博士學位. 主要研究方向為圖像/視頻壓縮與處理, 計算機視覺和人工智能. E-mail: hanzhen_2003@hotmail.com

                      陳軍:武漢大學教授. 主要研究方向為多媒體分析, 計算機視覺和安防應急信息處理. E-mail: chenj@whu.edu.cn

                      胡瑞敏:武漢大學教授. 主要研究方向為多媒體技術與大數據分析, 多媒體信號處理, 音視頻處理, 模式識別, 人工智能. E-mail: hrm1964@163.com

                    A Survey on Multi-source Person Re-identification

                    Funds: Supported by National Key Program of China (2017YFC0803700), National Natureal Science Foundation of China (61801335, 61876135), and Natural Science Foundation of Hubei Province(2018CFA024, 2019CFB472, 2018AAA062)
                    • 摘要: 行人重識別是近年來計算機視覺領域的熱點問題, 經過多年的發展, 基于可見光圖像的一般行人重識別技術已經趨近成熟. 然而, 目前的研究多基于一個相對理想的假設, 即行人圖像都是在光照充足的條件下拍攝的高分辨率圖像. 因此雖然大多數的研究都能取得較為滿意的效果, 但在實際環境中并不適用. 多源數據行人重識別即利用多種行人信息進行行人匹配的問題. 除了需要解決一般行人重識別所面臨的問題外, 多源數據行人重識別技術還需要解決不同類型行人信息與一般行人圖片相互匹配時的差異問題, 如低分辨率圖像、紅外圖像、深度圖像、文本信息和素描圖像等. 因此, 與一般行人重識別方法相比, 多源數據行人重識別研究更具實用性, 同時也更具有挑戰性. 本文首先介紹了一般行人重識別的發展現狀和所面臨的問題, 然后比較了多源數據行人重識別與一般行人重識別的區別, 并根據不同數據類型總結了5 類多源數據行人重識別問題, 分別從方法、數據集兩個方面對現有工作做了歸納和分析. 與一般行人重識別技術相比, 多源數據行人重識別的優點是可以充分利用各類數據學習跨模態和類型的特征轉換. 最后, 本文討論了多源數據行人重識別未來的發展.
                    • 圖  1  行人重識別示意圖

                      Fig.  1  An example illustrating person re-identification

                      圖  2  多源數據行人重識別類型

                      Fig.  2  Scope of multi-source data person re-identification studied in this survey

                      圖  3  一般行人重識別與多源數據行人重識別論文數量和最優效果對比

                      Fig.  3  The state-of-the-art performance and number of papers between general Re-ID and multi-source data Re-ID

                      圖  4  三類多源數據行人重識別方法描述

                      Fig.  4  Three types of methods for multi-source data re-ID

                      表  1  一般行人重識別與多源數據行人重識別的對比

                      Table  1  Comparison of general Re-ID and multi-source data Re-ID

                      一般行人重識別 多源數據行人重識別
                      定義 給定一個監控行人圖像, 檢索跨設備下的該行人圖像的技術 給定一個監控行人的跨類型或模態信息/圖像, 檢索跨設備跨模態下的該行人圖像的技術
                      數據類型 單一類型的圖像 多類型的圖像/視頻、文本、語言、素描等數據信息
                      方法 針對輸入圖像提取穩定、魯棒且能描述和區分不同行人的特征信息, 計算特征相似性, 根據相似性大小排序 使用特定于類型/域的網絡提取該類型/域的特征信息, 通過共享網絡生成特征, 使用合適的損失函數進行訓練并與普通網絡相連確保重識別工作的有效性
                      數據集 單一的可見光圖像、二分類屬性數據集 多種圖像、多種信息、多屬性數據集
                      解決重點和難點 低分辨率、視角和姿勢變化、光照變化、遮擋和視覺模糊性問題 模態變化以及一般行人重識別需要克服的問題
                      下載: 導出CSV

                      表  2  多源數據行人重識別工作中的代表性方法

                      Table  2  A summary of representational methods in multi-source data Re-ID

                      方法 模態 年份 會議/期刊 方法類別 數據集 度量學習 特征模型 統一模態
                      JUDEA[7] 高?低分辨率圖像 2015 ICCV 度量學習 ⑩?? × ×
                      SLD2L[9] 2015 CVPR 字典學習 ??? × ×
                      SALR-REID[8] 2016 IJCAI 子空間學習 ⑩?? ×
                      SING[14] 2018 AAAI 超分辨率 ??? ×
                      CSR-GAN[15] 2018 IJCAI 超分辨率 ⑩?? ×
                      DSPDL[11] 2018 AAAI 字典學習 ??? × ×
                      Zhuang[18] 2018 CVPR 深度對偶學習 ??? ×
                      Wu[22] 紅外?可見光圖像 2017 ICCV 深度零填充 ? × ×
                      TONE[24] 2018 AAAI 度量學習 ? ×
                      Ye[23] 2018 IJCAI 特征學習 ?? ×
                      cmGAN[25] 2018 IJCAI 特征嵌入 ? × ×
                      D2RL[26] 2019 CVPR 圖像生成 ?? ×
                      Barbosa[27] 深度?可見光圖像 2012 ECCV 度量學習 ? × ×
                      Wu[30] 2017 TIP 子空間學習 ??? ×
                      Hafner[31] 2018 CVPR 模態轉移 ?? ×
                      Ye[40] 文本?可見光圖像 2015 ACM 度量學習 ①④? × ×
                      Shi[35] 2015 CVPR 屬性識別 ①⑤? × ×
                      APR[37] 2017 CVPR 屬性識別 ⑦⑧ × ×
                      GNA-RNN[42] 2017 CVPR 密切關系學習 ? × ×
                      CNN-LSTM[41] 2017 ICCV 特征學習 ? × ×
                      MTL-LORAE[39] 2018 PAMI 特征學習 ①③④⑨ ×
                      Pang[45] 素描?可見光圖像 2018 ACM MM 特征學習 ? × ×
                      下載: 導出CSV

                      表  3  常用的一般行人重識別數據集與跨模態行人重識別數據集

                      Table  3  A summary of general Re-ID dataset and multi-source data Re-ID datase

                      類別 數據集名稱 發布時間 數據集類型 人數 相機數量 數據集大小
                      一般行人數據集 ①VIPeR[51] 2008 真實數據集 632 2 1 264幅 RGB 圖像
                      ②3DPES[52] 2011 192 8 1 011 幅 RGB 圖像
                      ③i-LIDS[50] 2009 119 2 476 幅 RGB 圖像
                      ④PRID2011[53] 2011 934 2 1 134 幅 RGB 圖像
                      ⑤CUHK01[48] 2012 971 2 3 884幅 RGB 圖像
                      ⑥CUHK03[6] 2014 1 467 10 13 164幅 RGB 圖像
                      ⑦Market-1501[54] 2015 1 501 6 32 217 幅 RGB 圖像
                      ⑧DukeMT MC-REID[55] 2017 1 812 8 36 441 幅 RGB 圖像
                      ⑨SAIVT-SoftBio[56] 2012 152 8 64 472 幅 RGB 圖像
                      低分辨率行人數據集 ⑩CAVIAR[57] 2011 真實數據集 72 2 720 幅高分辨率圖像
                      500 幅低分辨率圖像
                      ?LR-VIPeR[7, 9-11] 2015 模擬數據集 632 2 1 264 幅 RGB 圖像
                      ?LR-3DPES[7] 2015 192 8 1 011 幅 RGB 圖像
                      ?LR-PRID2011[9, 15] 2015 100 2 200 幅 RGB 圖像
                      ?LR-i-LDIS[9, 11] 2015 119 2 238 幅 RGB 圖像
                      ?SALR-VIPeR[8, 15] 2016 632 2 1 264 幅 RGB 圖像
                      ?SALR-PRID[8, 15] 2016 450 2 900 幅 RGB 圖像
                      ?MLR-VIPeR[14] 2018 632 2 1 264 幅 RGB 圖像
                      ?MLR-SYSU[14] 2018 502 2 3 012 幅 RGB 圖像
                      ?MLR-CUHK03[14] 2018 1 467 2 14 000 幅 RGB 圖像
                      ?LR-CUHK01[11] 2018 971 2 1 942 幅 RGB 圖像
                      ?LR-CUHK03[18] 2018 1 467 10 13 164 幅 RGB 圖像
                      ?LR-Market-1501[18] 2018 1 501 6 32 217 幅 RGB 圖像
                      ?LR-DukeMTMC-REID[18] 2018 1 812 8 36 441 幅 RGB 圖像
                      紅外行人數據集 ?SYSU-MM01[22] 2017 真實數據集 491 6 287 628 幅 RGB 圖像
                      15 792幅紅外圖像
                      ?RegDB[58] 2017 412 2 4 120 幅 RGB 圖像
                      4 120 幅紅外圖像
                      深度圖像行人數據集 ?PAVIS[27] 2012 真實數據集 79 ? 316 組視頻序列
                      ?BIWI RGBD-ID[28] 2014 50 ? 22 038 幅 RGB-D 圖像
                      ?IAS-Lab RGBD-ID[28] 2014 11 ? 33 個視頻序列
                      ?Kinect REID[59] 2016 71 ? 483 個視頻序列
                      ?RobotPKU RGBD-ID[60] 2017 90 ? 16 512 幅 RGB-D 圖像
                      文本行人數據集 ?PETA[34] 2014 真實數據集 8 705 ? 19 000 幅圖像
                      66 類文字標簽
                      ?CUHK-PEDES[42] 2017 13 003 ? 40 206 幅圖像
                      80 412 個句子描述
                      素描行人數據集 ?Sketch Re-ID[45] 2018 真實數據集 200 2 400 幅 RGB 圖像
                      200 幅素描
                      下載: 導出CSV

                      表  4  幾種多源數據行人重識別方法在常用的行人數據集上的識別結果

                      Table  4  Comparison of state-of-the-art methods on infra-red person re-identification dataset

                      數據集 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
                      紅外 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
                      深度圖像 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
                      下載: 導出CSV
                      360彩票
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                    • 收稿日期:  2019-04-01
                    • 錄用日期:  2019-10-17
                    • 網絡出版日期:  2020-09-28
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