专知荟萃26行人重识别 Person Re-identification知识资料全集（入门/进阶/论文/综述/代码，附查看）

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行人重识别 Person Re-identification / Person Retrieval 专知荟萃

  • 行人重识别 Person Re-identification / Person Retrieval 专知荟萃

  • 入门学习

  • 进阶论文及代码

    • Person Re-identification / Person Retrieval

    • Person Search

    • Re-ID with GAN

    • Vehicle Re-ID

    • Deep Metric Learning

    • Re-ID with Attributes Prediction

    • Video-based Person Re-Identification

    • Re-ranking

  • 实战项目

  • 教程

  • 综述

  • 数据集

    • 图像数据集

    • Attribute相关数据集

    • 视频相关数据集

    • NLP相关数据集

  • 领域专家


入门学习

  1. 行人重识别综述

    • [http://www.jianshu.com/p/98cc04cca0ae?utm_campaign=maleskine&utm_content=note&utm_medium=seo_notes&utm_source=recommendation\]

  2. 基于深度学习的Person Re-ID（综述）

    • [http://blog.csdn.net/linolzhang/article/details/71075756]

  3. 郑哲东 -Deep-ReID：行人重识别的深度学习方法

    • PPT：[https://www.slideshare.net/ZhedongZheng1/deep-reid]

    • 视频：[http://www.bilibili.com/video/av13796843/]

  4. 【行人识别】Deep Transfer Learning for Person Re-identification

    • [http://blog.csdn.net/shenxiaolu1984/article/details/53607268]

  5. 知乎专栏：行人重识别 [https://zhuanlan.zhihu.com/personReid]

    • 行人重识别综述：从哈利波特地图说起

    • 行人再识别中的迁移学习：图像风格转换（Learning via Translation）

    • 行人对齐+重识别网络

    • SVDNet for Pedestrian Retrieval：CNN到底认为哪个投影方向是重要的？

    • 用GAN生成的图像做训练？Yes！

    • 2017 ICCV 行人检索／重识别 接受论文汇总

    • 从人脸识别 到 行人重识别，下一个风口

  6. GAN（生成式对抗网络）的研究现状，以及在行人重识别领域的应用前景？

    • [https://www.zhihu.com/question/53001881/answer/170077548]

  7. Re-id Resources

    • [https://wangzwhu.github.io/home/re_id_resources.html\]

  8. 行人再识别（行人重识别）【包含与行人检测的对比】

    • [http://blog.csdn.net/liuqinglong110/article/details/41699861]

  9.  行人重识别综述（Person Re-identification: Past, Present and Future）

    • [http://blog.csdn.net/auto1993/article/details/74091803]


进阶论文及代码

Person Re-identification / Person Retrieval

  1. DeepReID: Deep Filter Pairing Neural Network for Person Re-Identification

    • intro: CVPR 2014

    • paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Li_DeepReID_Deep_Filter_2014_CVPR_paper.pdf]

  2. An Improved Deep Learning Architecture for Person Re-Identification

    • intro: CVPR 2015

    • paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Ahmed_An_Improved_Deep_2015_CVPR_paper.pdf]

    • github: [https://github.com/Ning-Ding/Implementation-CVPR2015-CNN-for-ReID]

  3. Deep Ranking for Person Re-identification via Joint Representation Learning

    • intro: IEEE Transactions on Image Processing [TIP], 2016

    • arxiv: [https://arxiv.org/abs/1505.06821]

  4. PersonNet: Person Re-identification with Deep Convolutional Neural Networks

    • arxiv: [http://arxiv.org/abs/1601.07255]

  5. Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification

    • intro: CVPR 2016

    • arxiv: [https://arxiv.org/abs/1604.07528]

    • github: [https://github.com/Cysu/dgd_person_reid]

  6. Person Re-Identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function

    • intro: CVPR 2016

    • paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Cheng_Person_Re-Identification_by_CVPR_2016_paper.pdf]

  7. End-to-End Comparative Attention Networks for Person Re-identification

    • [https://arxiv.org/abs/1606.04404]

  8. A Multi-task Deep Network for Person Re-identification

    • arxiv: [http://arxiv.org/abs/1607.05369]

  9. Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

    • arxiv: [http://arxiv.org/abs/1607.08378]

  10. A Siamese Long Short-Term Memory Architecture for Human Re-Identification

    • arxiv: [http://arxiv.org/abs/1607.08381]

  11. Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

    • arxiv: [https://arxiv.org/abs/1607.08378]

  12. Person Re-identification: Past, Present and Future

    • [https://arxiv.org/abs/1610.02984]

  13. Deep Learning Prototype Domains for Person Re-Identification

    • arxiv: [https://arxiv.org/abs/1610.05047]

  14. Deep Transfer Learning for Person Re-identification

    • arxiv: [https://arxiv.org/abs/1611.05244]

  15. A Discriminatively Learned CNN Embedding for Person Re-identification

    • arxiv: [https://arxiv.org/abs/1611.05666]

    • github[MatConvnet]: [https://github.com/layumi/2016_person_re-ID]

  16. Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification

    • arxiv: [https://arxiv.org/abs/1702.04179]

  17. In Defense of the Triplet Loss for Person Re-Identification

    • arxiv: [https://arxiv.org/abs/1703.07737]

    • github[Theano]: [https://github.com/VisualComputingInstitute/triplet-reid]

  18. Beyond triplet loss: a deep quadruplet network for person re-identification

    • intro: CVPR 2017

    • arxiv: [https://arxiv.org/abs/1704.01719]

  19. Part-based Deep Hashing for Large-scale Person Re-identification

    • intro: IEEE Transactions on Image Processing, 2017

    • arxiv: [https://arxiv.org/abs/1705.02145]

  20. Deep Person Re-Identification with Improved Embedding

    • [https://arxiv.org/abs/1705.03332]

  21. Towards a Principled Integration of Multi-Camera Re-Identification and Tracking through Optimal Bayes Filters

    • arxiv: [https://arxiv.org/abs/1705.04608]

    • github: [https://github.com/VisualComputingInstitute/towards-reid-tracking]

  22. Person Re-Identification by Deep Joint Learning of Multi-Loss Classification

    • intro: IJCAI 2017

    • arxiv: [https://arxiv.org/abs/1705.04724]

  23. Attention-based Natural Language Person Retrieval

    • intro: CVPR 2017 Workshop [vision meets cognition]

    • keywords: Bidirectional Long Short- Term Memory [BLSTM]

    • arxiv: [https://arxiv.org/abs/1705.08923]

  24. Unsupervised Person Re-identification: Clustering and Fine-tuning

    • arxiv: [https://arxiv.org/abs/1705.10444]

    • github: [https://github.com/hehefan/Unsupervised-Person-Re-identification-Clustering-and-Fine-tuning]

  25. Deep Representation Learning with Part Loss for Person Re-Identification

    • [https://arxiv.org/abs/1707.00798]

  26. Pedestrian Alignment Network for Large-scale Person Re-identification

    • [https://raw.githubusercontent.com/layumi/Pedestrian_Alignment/master/fig2.jpg]

    • arxiv: [https://arxiv.org/abs/1707.00408]

    • github: [https://github.com/layumi/Pedestrian_Alignment]

  27. Deep Reinforcement Learning Attention Selection for Person Re-Identification

    • [https://arxiv.org/abs/1707.02785]

  28. Learning Efficient Image Representation for Person Re-Identification

    • [https://arxiv.org/abs/1707.02319]

  29. Person Re-identification Using Visual Attention

    • intro: ICIP 2017

    • arxiv: [https://arxiv.org/abs/1707.07336]

  30. Deeply-Learned Part-Aligned Representations for Person Re-Identification

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1707.07256]

  31. What-and-Where to Match: Deep Spatially Multiplicative Integration Networks for Person Re-identification

    • [https://arxiv.org/abs/1707.07074]

  32. Deep Feature Learning via Structured Graph Laplacian Embedding for Person Re-Identification

    • [https://arxiv.org/abs/1707.07791]

  33. Divide and Fuse: A Re-ranking Approach for Person Re-identification

    • intro: BMVC 2017

    • arxiv: [https://arxiv.org/abs/1708.04169]

  34. Large Margin Learning in Set to Set Similarity Comparison for Person Re-identification

    • intro: IEEE Transactions on Multimedia

    • arxiv: [https://arxiv.org/abs/1708.05512]

  35. Multi-scale Deep Learning Architectures for Person Re-identification

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1709.05165]

  36. Pose-driven Deep Convolutional Model for Person Re-identification

    • [https://arxiv.org/abs/1709.08325]

  37. HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis

    • intro: ICCV 2017. CUHK & SenseTime,

    • arxiv: [https://arxiv.org/abs/1709.09930]

    • github: [https://github.com/xh-liu/HydraPlus-Net]

  38. Person Re-Identification with Vision and Language

    • [https://arxiv.org/abs/1710.01202]

  39. Margin Sample Mining Loss: A Deep Learning Based Method for Person Re-identification

    • [https://arxiv.org/abs/1710.00478]

  40. Learning Deep Context-aware Features over Body and Latent Parts for Person Re-identification

    • intro: CVPR 2017. CASIA

    • keywords: Multi-Scale Context-Aware Network [MSCAN]

    • arxiv: [https://arxiv.org/abs/1710.06555]

  41. Pseudo-positive regularization for deep person re-identification

    • [https://arxiv.org/abs/1711.06500]

  42. Let Features Decide for Themselves: Feature Mask Network for Person Re-identification

    • keywords: Feature Mask Network [FMN]

    • arxiv: [https://arxiv.org/abs/1711.07155]

  43. Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification

    • [https://arxiv.org/abs/1711.07027]

  44. AlignedReID: Surpassing Human-Level Performance in Person Re-Identification

    • intro: Megvii, Inc & Zhejiang University

    • arxiv: [https://arxiv.org/abs/1711.08184]

    • evaluation website: [Market1501]: [http://reid-challenge.megvii.com/]

    • evaluation website: [CUHK03]: [http://reid-challenge.megvii.com/cuhk03]

  45. Region-based Quality Estimation Network for Large-scale Person Re-identification

    • intro: AAAI 2018

    • arxiv: [https://arxiv.org/abs/1711.08766]

  46. Deep-Person: Learning Discriminative Deep Features for Person Re-Identification

    • [https://arxiv.org/abs/1711.10658]

  47. A Pose-Sensitive Embedding for Person Re-Identification with Expanded Cross Neighborhood Re-Ranking

    • arxiv: [https://arxiv.org/abs/1711.10378]

    • github: [https://github.com/pse-ecn/pose-sensitive-embedding]


Person Search

  1. Joint Detection and Identification Feature Learning for Person Search

    • intro: CVPR 2017

    • keywords: Online Instance Matching OIM loss function

    • homepage[dataset+code]:[http://www.ee.cuhk.edu.hk/~xgwang/PS/dataset.html]

    • arxiv: [https://arxiv.org/abs/1604.01850]

    • paper: [http://www.ee.cuhk.edu.hk/~xgwang/PS/paper.pdf]

    • github[official. Caffe]: [https://github.com/ShuangLI59/person_search]

  2. Person Re-identification in the Wild

    • intro: CVPR 2017 spotlight

    • keywords: PRW dataset

    • project page: [http://www.liangzheng.com.cn/Project/project_prw.html]

    • arxiv: [https://arxiv.org/abs/1604.02531]

    • github: [https://github.com/liangzheng06/PRW-baseline]

  3. IAN: The Individual Aggregation Network for Person Search

    • [https://arxiv.org/abs/1705.05552]

  4. Neural Person Search Machines

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1707.06777]


Re-ID with GAN

  1. Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1701.07717]

    • github: [https://github.com/layumi/Person-reID_GAN]

  2. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification

    • [https://arxiv.org/abs/1711.08565]


Vehicle Re-ID

  1. Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1708.03918]


Deep Metric Learning

  1. Deep Metric Learning for Person Re-Identification

    • intro: ICPR 2014

    • paper: [http://www.cbsr.ia.ac.cn/users/zlei/papers/ICPR2014/Yi-ICPR-14.pdf]

  2. Deep Metric Learning for Practical Person Re-Identification

    • [https://arxiv.org/abs/1407.4979]

  3. Constrained Deep Metric Learning for Person Re-identification

    • [https://arxiv.org/abs/1511.07545]

  4. DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer

    • intro: TuSimple

    • keywords: pedestrian re-identification

    • arxiv: [https://arxiv.org/abs/1707.01220]


Re-ID with Attributes Prediction

  1. Deep Attributes Driven Multi-Camera Person Re-identification

    • intro: ECCV 2016

    • arxiv: [https://arxiv.org/abs/1605.03259]

  2. Improving Person Re-identification by Attribute and Identity Learning

    • [https://arxiv.org/abs/1703.07220]


Video-based Person Re-Identification

  1. Recurrent Convolutional Network for Video-based Person Re-Identification

    • intro: CVPR 2016

    • paper: [http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/McLaughlin_Recurrent_Convolutional_Network_CVPR_2016_paper.pdf]

    • github: [https://github.com/niallmcl/Recurrent-Convolutional-Video-ReID]

  2. Deep Recurrent Convolutional Networks for Video-based Person Re-identification: An End-to-End Approach

    • [https://arxiv.org/abs/1606.01609]

  3. Jointly Attentive Spatial-Temporal Pooling Networks for Video-based Person Re-Identification

    • intro: ICCV 2017

    • arxiv: [https://arxiv.org/abs/1708.02286]

  4. Three-Stream Convolutional Networks for Video-based Person Re-Identification

    • [https://arxiv.org/abs/1712.01652]


Re-ranking

  1. Re-ranking Person Re-identification with k-reciprocal Encoding

    • intro: CVPR 2017

    • arxiv: [https://arxiv.org/abs/1701.08398]

    • github: [https://github.com/zhunzhong07/person-re-ranking]


实战项目

  1. Open-ReID: Open source person re-identification library in python

    • intro: Open-ReID is a lightweight library of person re-identification for research purpose. It aims to provide a uniform interface for different datasets, a full set of models and evaluation metrics, as well as examples to reproduce [near] state-of-the-art results.

    • project page: [https://cysu.github.io/open-reid/]

    • github[PyTorch]: [https://github.com/Cysu/open-reid]

    • examples: [https://cysu.github.io/open-reid/examples/training_id.html]

    • benchmarks: [https://cysu.github.io/open-reid/examples/benchmarks.html]

  2. caffe-PersonReID

    • intro: Person Re-Identification: Multi-Task Deep CNN with Triplet Loss

    • gtihub: [https://github.com/agjayant/caffe-Person-ReID]

  3. DukeMTMC-reID_baseline Matlab

    • [https://github.com/layumi/DukeMTMC-reID_baseline]

  4. Code for IDE baseline on Market-1501

    • [https://github.com/zhunzhong07/IDE-baseline-Market-1501]


教程

  1. 1st Workshop on Target Re-Identification and Multi-Target Multi-Camera Tracking

    • [https://reid-mct.github.io/]

  2. 郑哲东 -Deep-ReID：行人重识别的深度学习方法

    • PPT：[https://www.slideshare.net/ZhedongZheng1/deep-reid]

    • 视频：[http://www.bilibili.com/video/av13796843/]

  3. Person Identification in Large Scale Camera Networks Wei-Shi Zheng (郑伟诗)

    • [http://isee.sysu.edu.cn/~zhwshi/Research/ADL-OPEN.pdf\]

  4. Person Re-Identification: Theory and Best Practice

    • [http://www.micc.unifi.it/reid-tutorial/slides/]


综述

  1. Person Re-identification: Past, Present and Future Liang Zheng, Yi Yang, Alexander G. Hauptmann

    • [https://arxiv.org/abs/1610.02984]

  2. Person Re-Identification Book

    • [https://link.springer.com/book/10.1007/978-1-4471-6296-4]

  3. A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets

    • [http://lanl.arxiv.org/abs/1605.09653]

  4. People reidentification in surveillance and forensics: A survey

    • [https://dl.acm.org/citation.cfm?doid=2543581.2543596]


数据集

  1. Re-ID 数据集汇总

    • [https://robustsystems.coe.neu.edu/sites/robustsystems.coe.neu.edu/files/systems/projectpages/reiddataset.html]


图像数据集

  1. Market-1501 Dataset 751个人，27种属性，一共约三万张图像（一人多图）

    • [http://www.liangzheng.org/Project/project_reid.html\]

    • Code for IDE baseline on Market-1501 :[https://github.com/zhunzhong07/IDE-baseline-Market-1501]

    • [https://github.com/layumi/DukeMTMC-reID_evaluation\]

  3. CUHK01, 02, 03

    • [http://www.ee.cuhk.edu.hk/~rzhao/\]


Attribute相关数据集

  1. RAP

    • [https://link.zhihu.com/?target=http%3A//rap.idealtest.org/]

  2. Attribute for Market-1501and DukeMTMC_reID

    • [https://link.zhihu.com/?target=https%3A//vana77.github.io/]


视频相关数据集

  1. Mars

    • [http://liangzheng.org/Project/project_mars.html]

  2. PRID2011

    • [https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/]


NLP相关数据集：

  1. 自然语言搜图像

    • [http://xiaotong.me/static/projects/person-search-language/dataset.html]

  2. 自然语言搜索行人所在视频

    • [http://www.mi.t.u-tokyo.ac.jp/projects/person_search]


领域专家

  1. Shaogang Gong -[http://www.eecs.qmul.ac.uk/~sgg/\]

  2. Xiaogang Wang

    • [http://www.ee.cuhk.edu.hk/~xgwang/\]

  3. Weishi Zheng

    • [https://sites.google.com/site/sunnyweishi/]

  4. Liang Zheng

    • [http://www.liangzheng.com.cn/]

  5. Chen Change Loy

    • [https://staff.ie.cuhk.edu.hk/~ccloy/\]

  6. Qi Tian

    • [http://www.cs.utsa.edu/~qitian/tian-publication-year.html\]

  7. Shengcai Liao

    • [http://www.cbsr.ia.ac.cn/users/scliao/]

  8. Rui Zhao

    • [http://www.ee.cuhk.edu.hk/~rzhao/\]

  9. Yang Yang

    • [http://www.cbsr.ia.ac.cn/users/yyang/main.htm]

  10. Ling Shao

    • [http://lshao.staff.shef.ac.uk/]

  11. Ziyan Wu

    • [http://wuziyan.com/]

  12. DaPeng Chen

    • [http://gr.xjtu.edu.cn/web/dapengchen/home]

  13. Horst Bischof

    • [https://www.tugraz.at/institute/icg/research/team-bischof/lrs/downloads/prid450s]

  14. Niki Martinel

    • [http://users.dimi.uniud.it/~niki.martinel/\]

  15. Liang Lin

    • [http://hcp.sysu.edu.cn/home/]

  16. Le An

    • [http://auto.hust.edu.cn/index.php?a=shows&catid=28&id=134]

  17. Xiang Bai

    • [http://mc.eistar.net/~xbai/index.html\]

  18. Xiaoyuan Jing

    • [http://mla.whu.edu.cn/plus/list.php?tid=2]

  19. Fei Xiong

    • [http://robustsystems.coe.neu.edu/?q=content/research]

  20. DaPeng Chen

    • [http://gr.xjtu.edu.cn/web/dapengchen/home]

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