Although the performance of person Re-Identification (ReID) has been significantly boosted, many challenging issues in real scenarios have not been fully investigated, e.g., the complex scenes and lighting variations, viewpoint and pose changes, and the large number of identities in a camera network. To facilitate the research towards conquering those issues, this paper contributes a new dataset called MSMT17 with many important features, e.g., 1) the raw videos are taken by an 15-camera network deployed in both indoor and outdoor scenes, 2) the videos cover a long period of time and present complex lighting variations, and 3) it contains currently the largest number of annotated identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe that, domain gap commonly exists between datasets, which essentially causes severe performance drop when training and testing on different datasets. This results in that available training data cannot be effectively leveraged for new testing domains. To relieve the expensive costs of annotating new training samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to bridge the domain gap. Comprehensive experiments show that the domain gap could be substantially narrowed-down by the PTGAN.

近年来，随着对公共安全的需求越来越多，智能监测网络的快速发展，人员重新识别（RE-ID）已成为计算机视野领域的热门研究主题之一。人员RE-ID的主要研究目标是从不同的摄像机中检索具有相同身份的人。但是，传统的人重新ID方法需要手动标记人的目标，这消耗了大量的劳动力成本。随着深度神经网络的广泛应用，出现了许多基于深入的基于学习的人物的方法。因此，本文促进研究人员了解最新的研究成果和该领域的未来趋势。首先，我们总结了对几个最近公布的人的研究重新ID调查，并补充了系统地分类基于深度学习的人的重新ID方法的最新研究方法。其次，我们提出了一种多维分类，根据度量标准和表示学习，将基于深度学习的人的重新ID方法分为四类，包括深度度量学习，本地特征学习，生成的对抗学习和序列特征学习的方法。此外，我们根据其方法和动机来细分以上四类，讨论部分子类别的优缺点。最后，我们讨论了一些挑战和可能的研究方向的人重新ID。

Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt, we present a "learning via translation" framework. In the baseline, we translate the labeled images from source to target domain in an unsupervised manner. We then train re-ID models with the translated images by supervised methods. Yet, being an essential part of this framework, unsupervised image-image translation suffers from the information loss of source-domain labels during translation.Our motivation is two-fold. First, for each image, the discriminative cues contained in its ID label should be maintained after translation. Second, given the fact that two domains have entirely different persons, a translated image should be dissimilar to any of the target IDs. To this end, we propose to preserve two types of unsupervised similarities, 1) self-similarity of an image before and after translation, and 2) domain-dissimilarity of a translated source image and a target image. Both constraints are implemented in the similarity preserving generative adversarial network (SPGAN) which consists of an Siamese network and a Cy-cleGAN. Through domain adaptation experiment, we show that images generated by SPGAN are more suitable for domain adaptation and yield consistent and competitive re-ID accuracy on two large-scale datasets.

人重新识别（RE-ID）在公共安全和视频监控等应用中起着重要作用。最近，从合成数据引擎的普及中获益的合成数据学习，从公众眼中引起了极大的关注。但是，现有数据集数量，多样性和变性有限，并且不能有效地用于重新ID问题。为了解决这一挑战，我们手动构造一个名为FineGPR的大型人数据集，具有细粒度的属性注释。此外，旨在充分利用FineGPR的潜力，并推广从数百万综合数据的高效培训，我们提出了一个名为AOST的属性分析流水线，它动态地学习了真实域中的属性分布，然后消除了合成和现实世界之间的差距因此，自由地部署到新场景。在基准上进行的实验表明，FineGPR具有AOST胜过（或与）现有的实际和合成数据集，这表明其对重新ID任务的可行性，并证明了众所周知的较少的原则。我们的Synthetic FineGPR数据集可公开可用于\ URL {https://github.com/jeremyxsc/finegpr}。

Person re-identification (Re-ID) aims at retrieving a person of interest across multiple non-overlapping cameras. With the advancement of deep neural networks and increasing demand of intelligent video surveillance, it has gained significantly increased interest in the computer vision community. By dissecting the involved components in developing a person Re-ID system, we categorize it into the closed-world and open-world settings. The widely studied closed-world setting is usually applied under various research-oriented assumptions, and has achieved inspiring success using deep learning techniques on a number of datasets. We first conduct a comprehensive overview with in-depth analysis for closed-world person Re-ID from three different perspectives, including deep feature representation learning, deep metric learning and ranking optimization. With the performance saturation under closed-world setting, the research focus for person Re-ID has recently shifted to the open-world setting, facing more challenging issues. This setting is closer to practical applications under specific scenarios. We summarize the open-world Re-ID in terms of five different aspects. By analyzing the advantages of existing methods, we design a powerful AGW baseline, achieving state-of-the-art or at least comparable performance on twelve datasets for FOUR different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP) for person Re-ID, indicating the cost for finding all the correct matches, which provides an additional criteria to evaluate the Re-ID system for real applications. Finally, some important yet under-investigated open issues are discussed.

The main contribution of this paper is a simple semisupervised pipeline that only uses the original training set without collecting extra data. It is challenging in 1) how to obtain more training data only from the training set and 2) how to use the newly generated data. In this work, the generative adversarial network (GAN) is used to generate unlabeled samples. We propose the label smoothing regularization for outliers (LSRO). This method assigns a uniform label distribution to the unlabeled images, which regularizes the supervised model and improves the baseline.We verify the proposed method on a practical problem: person re-identification (re-ID). This task aims to retrieve a query person from other cameras. We adopt the deep convolutional generative adversarial network (DCGAN) for sample generation, and a baseline convolutional neural network (CNN) for representation learning. Experiments show that adding the GAN-generated data effectively improves the discriminative ability of learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1 precision over the baseline CNN, respectively. We additionally apply the proposed method to fine-grained bird recognition and achieve a +0.6% improvement over a strong baseline. The code is available at https://github.com/layumi/Person-reID_GAN .

最近，无监督的人重新识别（RE-ID）引起了人们的关注，因为其开放世界情景设置有限，可用的带注释的数据有限。现有的监督方法通常无法很好地概括在看不见的域上，而无监督的方法（大多数缺乏多范围的信息），并且容易患有确认偏见。在本文中，我们旨在从两个方面从看不见的目标域上找到更好的特征表示形式，1）在标记的源域上进行无监督的域适应性和2）2）在未标记的目标域上挖掘潜在的相似性。此外，提出了一种协作伪标记策略，以减轻确认偏见的影响。首先，使用生成对抗网络将图像从源域转移到目标域。此外，引入了人身份和身份映射损失，以提高生成图像的质量。其次，我们提出了一个新颖的协作多元特征聚类框架（CMFC），以学习目标域的内部数据结构，包括全局特征和部分特征分支。全球特征分支（GB）在人体图像的全球特征上采用了无监督的聚类，而部分特征分支（PB）矿山在不同人体区域内的相似性。最后，在两个基准数据集上进行的广泛实验表明，在无监督的人重新设置下，我们的方法的竞争性能。

具有大量空间和时间跨境的情景中的人重新识别（RE-ID）尚未完全探索。这部分原因是，现有的基准数据集主要由有限的空间和时间范围收集，例如，使用在校园特定区域的相机录制的视频中使用的视频。这种有限的空间和时间范围使得难以模拟真实情景中的人的困难。在这项工作中，我们贡献了一个新的大型时空上次最后一个数据集，包括10,862个图像，具有超过228k的图像。与现有数据集相比，最后一个具有挑战性和高度多样性的重新ID设置，以及显着更大的空间和时间范围。例如，每个人都可以出现在不同的城市或国家，以及在白天到夜间的各个时隙，以及春季到冬季的不同季节。为了我们的最佳知识，最后是一个新的Perse Re-ID数据集，具有最大的时空范围。基于最后，我们通过对14个RE-ID算法进行全面的绩效评估来验证其挑战。我们进一步提出了一种易于实施的基线，适用于如此挑战的重新ID设置。我们还验证了初步训练的模型可以在具有短期和更改方案的现有数据集中概括。我们期待持续激发未来的工程，以更现实和挑战的重新识别任务。有关DataSet的更多信息，请访问https://github.com/shuxjweb/last.git。

This paper contributes a new high quality dataset for person re-identification, named "Market-1501". Generally, current datasets: 1) are limited in scale; 2) consist of hand-drawn bboxes, which are unavailable under realistic settings; 3) have only one ground truth and one query image for each identity (close environment). To tackle these problems, the proposed Market-1501 dataset is featured in three aspects. First, it contains over 32,000 annotated bboxes, plus a distractor set of over 500K images, making it the largest person re-id dataset to date. Second, images in Market-1501 dataset are produced using the Deformable Part Model (DPM) as pedestrian detector. Third, our dataset is collected in an open system, where each identity has multiple images under each camera.As a minor contribution, inspired by recent advances in large-scale image search, this paper proposes an unsupervised Bag-of-Words descriptor. We view person reidentification as a special task of image search. In experiment, we show that the proposed descriptor yields competitive accuracy on VIPeR, CUHK03, and Market-1501 datasets, and is scalable on the large-scale 500k dataset.

When considering person re-identification (re-ID) as a retrieval process, re-ranking is a critical step to improve its accuracy. Yet in the re-ID community, limited effort has been devoted to re-ranking, especially those fully automatic, unsupervised solutions. In this paper, we propose a -reciprocal encoding method to re-rank the re-ID results. Our hypothesis is that if a gallery image is similar to the probe in the -reciprocal nearest neighbors, it is more likely to be a true match. Specifically, given an image, areciprocal feature is calculated by encoding its -reciprocal nearest neighbors into a single vector, which is used for reranking under the Jaccard distance. The final distance is computed as the combination of the original distance and the Jaccard distance. Our re-ranking method does not require any human interaction or any labeled data, so it is applicable to large-scale datasets. Experiments on the largescale Market-1501, CUHK03, MARS, and PRW datasets confirm the effectiveness of our method 1 .

本文研究了行人图像的新型隐私匿名问题，该问题保留了授权模型的个人身份信息（PII），并防止PII被第三方认可。常规的匿名方法不可避免地会导致语义信息丢失，从而导致数据实用性有限。此外，现有的学习匿名技术，同时保留各种身份 - 艾尔特尔维坦公用事业，将改变行人身份，因此不适合培训强大的重新识别模型。为了探索行人图像的隐私 - 实用性权衡取舍，我们提出了一个联合学习可逆的匿名框架，该框架可以可逆地生成全身匿名图像，而对人员重新识别任务的性能很小。核心思想是，我们采用常规方法生成的脱敏图像作为初始隐私的监督，并共同训练具有恢复解码器和身份不变模型的匿名编码器。我们进一步提出了一种渐进培训策略来改善绩效，迭代地升级了最初的匿名监督。实验进一步证明了我们的匿名行人图像对隐私保护的有效性，这在保留隐私时提高了重新识别性能。代码可在\ url {https://github.com/whuzjw/privacy-reid}中获得。

In this paper, we present the Multi-view Extended Videos with Identities (MEVID) dataset for large-scale, video person re-identification (ReID) in the wild. To our knowledge, MEVID represents the most-varied video person ReID dataset, spanning an extensive indoor and outdoor environment across nine unique dates in a 73-day window, various camera viewpoints, and entity clothing changes. Specifically, we label the identities of 158 unique people wearing 598 outfits taken from 8, 092 tracklets, average length of about 590 frames, seen in 33 camera views from the very large-scale MEVA person activities dataset. While other datasets have more unique identities, MEVID emphasizes a richer set of information about each individual, such as: 4 outfits/identity vs. 2 outfits/identity in CCVID, 33 viewpoints across 17 locations vs. 6 in 5 simulated locations for MTA, and 10 million frames vs. 3 million for LS-VID. Being based on the MEVA video dataset, we also inherit data that is intentionally demographically balanced to the continental United States. To accelerate the annotation process, we developed a semi-automatic annotation framework and GUI that combines state-of-the-art real-time models for object detection, pose estimation, person ReID, and multi-object tracking. We evaluate several state-of-the-art methods on MEVID challenge problems and comprehensively quantify their robustness in terms of changes of outfit, scale, and background location. Our quantitative analysis on the realistic, unique aspects of MEVID shows that there are significant remaining challenges in video person ReID and indicates important directions for future research.

计算机视觉的挑战之一是它需要适应可变环境中的颜色偏差。因此，将颜色偏差对预测的不利影响最小化是视觉任务的主要目标之一。当前的解决方案着重于使用生成模型增强训练数据以增强输入变化的不变性。但是，这种方法通常会引入新的噪声，从而限制了生成数据的增益。为此，本文提出了一种策略，消除了偏差的偏差，该偏差称为随机颜色辍学（RCD）。我们的假设是，如果查询图像和画廊图像之间存在颜色偏差，那么在忽略颜色信息之后，某些示例的检索结果会更好。具体而言，该策略通过在训练数据中辍学的部分颜色信息来平衡神经网络中颜色特征和无关的特征之间的权重，以克服颜色devitaion的效果。所提出的RCD可以与各种现有的REID模型相结合而不更改学习策略，并且可以应用于其他计算机视野字段，例如对象检测。在几个REID基线和三个常见的大规模数据集（例如Market1501，Dukemtmc和MSMT17）上进行的实验已验证了该方法的有效性。跨域测试的实验表明，该策略显着消除了域间隙。此外，为了了解RCD的工作机制，我们从分类的角度分析了该策略的有效性，这表明在具有强大域变化的视觉任务中，最好利用许多而不是所有颜色信息。

人的步态被认为是一种独特的生物识别标识符，其可以在距离处以覆盖方式获取。但是，在受控场景中捕获的现有公共领域步态数据集接受的模型导致应用于现实世界无约束步态数据时的剧烈性能下降。另一方面，视频人员重新识别技术在大规模公共可用数据集中实现了有希望的性能。鉴于服装特性的多样性，衣物提示对于人们的认可不可靠。因此，实际上尚不清楚为什么最先进的人重新识别方法以及他们的工作。在本文中，我们通过从现有的视频人重新识别挑战中提取剪影来构建一个新的步态数据集，该挑战包括1,404人以不受约束的方式行走。基于该数据集，可以进行步态认可与人重新识别之间的一致和比较研究。鉴于我们的实验结果表明，目前在受控情景收集的数据下设计的目前的步态识别方法不适合真实监视情景，我们提出了一种名为Realgait的新型步态识别方法。我们的结果表明，在实际监视情景中识别人的步态是可行的，并且潜在的步态模式可能是视频人重新设计在实践中的真正原因。

由于其前所未有的优势，在规模，移动，部署和隐蔽观察能力方面，空中平台和成像传感器的快速出现是实现新的空中监测形式。本文从计算机视觉和模式识别的角度来看，全面概述了以人为本的空中监控任务。它旨在为读者提供使用无人机，无人机和其他空中平台的空中监测任务当前状态的深入系统审查和技术分析。感兴趣的主要对象是人类，其中要检测单个或多个受试者，识别，跟踪，重新识别并进行其行为。更具体地，对于这四项任务中的每一个，我们首先讨论与基于地面的设置相比在空中环境中执行这些任务的独特挑战。然后，我们审查和分析公共可用于每项任务的航空数据集，并深入了解航空文学中的方法，并调查他们目前如何应对鸟瞰挑战。我们在讨论缺失差距和开放研究问题的讨论中得出结论，告知未来的研究途径。

In this paper, we are interested in learning a generalizable person re-identification (re-ID) representation from unlabeled videos. Compared with 1) the popular unsupervised re-ID setting where the training and test sets are typically under the same domain, and 2) the popular domain generalization (DG) re-ID setting where the training samples are labeled, our novel scenario combines their key challenges: the training samples are unlabeled, and collected form various domains which do no align with the test domain. In other words, we aim to learn a representation in an unsupervised manner and directly use the learned representation for re-ID in novel domains. To fulfill this goal, we make two main contributions: First, we propose Cycle Association (CycAs), a scalable self-supervised learning method for re-ID with low training complexity; and second, we construct a large-scale unlabeled re-ID dataset named LMP-video, tailored for the proposed method. Specifically, CycAs learns re-ID features by enforcing cycle consistency of instance association between temporally successive video frame pairs, and the training cost is merely linear to the data size, making large-scale training possible. On the other hand, the LMP-video dataset is extremely large, containing 50 million unlabeled person images cropped from over 10K Youtube videos, therefore is sufficient to serve as fertile soil for self-supervised learning. Trained on LMP-video, we show that CycAs learns good generalization towards novel domains. The achieved results sometimes even outperform supervised domain generalizable models. Remarkably, CycAs achieves 82.2% Rank-1 on Market-1501 and 49.0% Rank-1 on MSMT17 with zero human annotation, surpassing state-of-the-art supervised DG re-ID methods. Moreover, we also demonstrate the superiority of CycAs under the canonical unsupervised re-ID and the pretrain-and-finetune scenarios.

Person re-identification plays a significant role in realistic scenarios due to its various applications in public security and video surveillance. Recently, leveraging the supervised or semi-unsupervised learning paradigms, which benefits from the large-scale datasets and strong computing performance, has achieved a competitive performance on a specific target domain. However, when Re-ID models are directly deployed in a new domain without target samples, they always suffer from considerable performance degradation and poor domain generalization. To address this challenge, we propose a Deep Multimodal Fusion network to elaborate rich semantic knowledge for assisting in representation learning during the pre-training. Importantly, a multimodal fusion strategy is introduced to translate the features of different modalities into the common space, which can significantly boost generalization capability of Re-ID model. As for the fine-tuning stage, a realistic dataset is adopted to fine-tune the pre-trained model for better distribution alignment with real-world data. Comprehensive experiments on benchmarks demonstrate that our method can significantly outperform previous domain generalization or meta-learning methods with a clear margin. Our source code will also be publicly available at https://github.com/JeremyXSC/DMF.

媒体中的人员搜索已经看到互联网应用程序的潜力，例如视频剪辑和字符集。这项任务很常见，但忽略了以前的人员搜索工作，专注于监视场景。媒体情景从监视场景中有一些不同的挑战。例如，一个人可能经常改变衣服。为了减轻这个问题，本文提出了一个统一的探测器和图形网络（UDGNET），用于媒体中的人员搜索。 UDGNET是第一个检测和重新识别人体和头部的第一个人搜索框架。具体地，它首先基于统一网络构建两个分支以检测人体和头部，然后检测到的主体和头部用于重新识别。这种双重任务方法可以显着增强歧视性学习。为了解决布料不断变化的问题，UDGNET构建了两个图形，以探索布换器样本中的可靠链接，并利用图形网络来学习更好的嵌入。这种设计有效地增强了人们搜索的鲁棒性，以改变布什挑战。此外，我们证明了UDGNET可以通过基于锚和无锚的人搜索框架来实现，并进一步实现性能改进。本文还为媒体（PSM）中的人员搜索提供了大规模数据集，其提供身体和头部注释。它是迄今为止媒体搜索的最大数据集。实验表明，UDGNET在MAP中通过12.1％提高了Anipor的模型。同时，它在监控和长期情景中显示出良好的概括。数据集和代码将可用：https://github.com/shuxjweb/psm.git。

人重新识别（人REID）模型的现有评估指标着重于系统范围的性能。但是，我们的研究揭示了由于摄像机之间的数据分布不平的弱点和将REID系统暴露于剥削的不同摄像头性能。在这项工作中，我们提出了长期以来的摄像机性能不平衡问题，并从38个摄像机中收集了现实世界中的隐私意识数据集，以帮助研究不平衡问题。我们提出了新的指标来量化摄像机性能不平衡，并进一步提出了对抗性成对的反向关注（APRA）模块，以指导模型学习摄像机不变特征，并具有新颖的成对注意反转机制。

Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers to develop and test many successful approaches. However, when such Re-ID models are deployed in new cities or environments, the task of searching for people within a network of security cameras is likely to face an important domain shift, thus resulting in decreased performance. Indeed, while most public datasets were collected in a limited geographic area, images from a new city present different features (e.g., people's ethnicity and clothing style, weather, architecture, etc.). In addition, the whole frames of the video streams must be converted into cropped images of people using pedestrian detection models, which behave differently from the human annotators who created the dataset used for training. To better understand the extent of this issue, this paper introduces a complete methodology to evaluate Re-ID approaches and training datasets with respect to their suitability for unsupervised deployment for live operations. This method is used to benchmark four Re-ID approaches on three datasets, providing insight and guidelines that can help to design better Re-ID pipelines in the future.