【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）

2017 年 12 月 10 日 专知专知内容组

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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

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\]

基于深度学习的Person Re-ID（综述）

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

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/]

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

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

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

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]

Re-id Resources

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

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]

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

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]

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]

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]

PersonNet: Person Re-identification with Deep Convolutional Neural Networks

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

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]

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]

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

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

A Multi-task Deep Network for Person Re-identification

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

Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

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

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

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

Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification

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

Person Re-identification: Past, Present and Future

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

Deep Learning Prototype Domains for Person Re-Identification

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

Deep Transfer Learning for Person Re-identification

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

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]

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

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

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]

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

intro: CVPR 2017
arxiv: [https://arxiv.org/abs/1704.01719]

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

intro: IEEE Transactions on Image Processing, 2017
arxiv: [https://arxiv.org/abs/1705.02145]

Deep Person Re-Identification with Improved Embedding

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

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]

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

intro: IJCAI 2017
arxiv: [https://arxiv.org/abs/1705.04724]

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]

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]

Deep Representation Learning with Part Loss for Person Re-Identification

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

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]

Deep Reinforcement Learning Attention Selection for Person Re-Identification

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

Learning Efficient Image Representation for Person Re-Identification

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

Person Re-identification Using Visual Attention

intro: ICIP 2017
arxiv: [https://arxiv.org/abs/1707.07336]

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

intro: ICCV 2017
arxiv: [https://arxiv.org/abs/1707.07256]

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

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

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

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

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

intro: BMVC 2017
arxiv: [https://arxiv.org/abs/1708.04169]

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]

Multi-scale Deep Learning Architectures for Person Re-identification

intro: ICCV 2017
arxiv: [https://arxiv.org/abs/1709.05165]

Pose-driven Deep Convolutional Model for Person Re-identification

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

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]

Person Re-Identification with Vision and Language

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

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

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

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]

Pseudo-positive regularization for deep person re-identification

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

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

keywords: Feature Mask Network [FMN]
arxiv: [https://arxiv.org/abs/1711.07155]

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

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

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]

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

intro: AAAI 2018
arxiv: [https://arxiv.org/abs/1711.08766]

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

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

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

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]

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]

IAN: The Individual Aggregation Network for Person Search

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

Neural Person Search Machines

intro: ICCV 2017
arxiv: [https://arxiv.org/abs/1707.06777]

Re-ID with GAN

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]

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

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

Vehicle Re-ID

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

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]

Deep Metric Learning for Practical Person Re-Identification

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

Constrained Deep Metric Learning for Person Re-identification

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

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

Deep Attributes Driven Multi-Camera Person Re-identification

intro: ECCV 2016
arxiv: [https://arxiv.org/abs/1605.03259]

Improving Person Re-identification by Attribute and Identity Learning

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

Video-based Person Re-Identification

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]

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

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

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

intro: ICCV 2017
arxiv: [https://arxiv.org/abs/1708.02286]

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

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

Re-ranking

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

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]

caffe-PersonReID

intro: Person Re-Identification: Multi-Task Deep CNN with Triplet Loss
gtihub: [https://github.com/agjayant/caffe-Person-ReID]

DukeMTMC-reID_baseline Matlab

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

Code for IDE baseline on Market-1501

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

教程

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

[https://reid-mct.github.io/]

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/]

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

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

Person Re-Identification: Theory and Best Practice

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

综述

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

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

Person Re-Identification Book

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

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

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

People reidentification in surveillance and forensics: A survey

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

数据集

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

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]

DukeMTMC-reID DukeMTMC数据集的行人重识别子集，原始数据集地址(http://vision.cs.duke.edu/DukeMTMC/) ，为行人跟踪数据集。原始数据集包含了85分钟的高分辨率视频，采集自8个不同的摄像头。并且提供了人工标注的bounding box。最终，DukeMTMC-reID 包含了 16,522张训练图片（来自702个人）， 2,228个查询图像（来自另外的702个人），以及 17,661 张图像的搜索库（gallery）。并提供切割后的图像供下载。