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。

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。

从图像中学习代表，健壮和歧视性信息对于有效的人重新识别（RE-ID）至关重要。在本文中，我们提出了一种基于身体和手部图像的人重新ID的端到端判别深度学习的复合方法。我们仔细设计了本地感知的全球注意力网络（Laga-Net），这是一个多分支深度网络架构，由一个用于空间注意力的分支组成，一个用于渠道注意。注意分支集中在图像的相关特征上，同时抑制了无关紧要的背景。为了克服注意力机制的弱点，与像素改组一样，我们将相对位置编码整合到空间注意模块中以捕获像素的空间位置。全球分支机构打算保留全球环境或结构信息。对于打算捕获细粒度信息的本地分支，我们进行统一的分区以水平在Conv-Layer上生成条纹。我们通过执行软分区来检索零件，而无需明确分区图像或需要外部线索，例如姿势估计。一组消融研究表明，每个组件都会有助于提高拉加网络的性能。对四个受欢迎的人体重新ID基准和两个公开可用的手数据集的广泛评估表明，我们的建议方法始终优于现有的最新方法。

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.

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.

由于其在智能城市和城市监测中的潜在应用，车辆重新ID最近引起了热烈的关注。然而，它遭受了通过观察变化和照明变化引起的大型阶级变化，以及阶级相似性，特别是对于具有类似外观的不同标识。为了处理这些问题，在本文中，我们提出了一种新颖的深度网络架构，其由有意义的属性引导，包括相机视图，车辆类型和用于车辆RE-ID的颜色。特别是，我们的网络是端到端训练的，并包含由相应属性嵌入的深度特征的三个子网（即，相机视图，车辆类型和车辆颜色）。此外，为了克服不同视图的有限载体图像的缺点，我们设计了一个视图指定的生成的对抗性网络来生成多视图车辆图像。对于网络培训，我们在Veri-776数据集上注释了视图标签。请注意，只能使用ID信息直接在其他数据集上直接在其他数据集上采用预先训练的视图（以及类型和颜色）子网，这展示了我们模型的泛化。基准数据集Veri-776和车辆的广泛实验表明，拟议的方法实现了有希望的性能，并对车辆重新ID的新型最先进的性能。

未经监督的人重新识别（Reid）是一个具有挑战性的任务，没有数据注释，以指导歧视性学习。现有方法通过群集提取的嵌入式来尝试解决此问题以生成伪标签。然而，大多数方法忽略了摄像机样式方差引起的类内间隙，并且一些方法是相对复杂和间接的，尽管它们试图解决相机样式对特征分布的负面影响。为了解决这个问题，我们提出了一种相机感知的风格分离和对比学习方法（CA-Ureid），它直接将相机样式与设计的相机感知的注意模块直接分离在功能空间中。它可以将学习功能明确地将学习功能分为特定于相机和相机不可知的部件，从而降低了不同摄像机的影响。此外，为了进一步缩小相机的差距，我们设计了一个摄像机感知对比中心损失，以了解每个身份的更多歧视性嵌入。广泛的实验证明了我们对无监督者Reid任务的最先进方法的方法的优越性。

Person re-identification is a challenging task because of the high intra-class variance induced by the unrestricted nuisance factors of variations such as pose, illumination, viewpoint, background, and sensor noise. Recent approaches postulate that powerful architectures have the capacity to learn feature representations invariant to nuisance factors, by training them with losses that minimize intra-class variance and maximize inter-class separation, without modeling nuisance factors explicitly. The dominant approaches use either a discriminative loss with margin, like the softmax loss with the additive angular margin, or a metric learning loss, like the triplet loss with batch hard mining of triplets. Since the softmax imposes feature normalization, it limits the gradient flow supervising the feature embedding. We address this by joining the losses and leveraging the triplet loss as a proxy for the missing gradients. We further improve invariance to nuisance factors by adding the discriminative task of predicting attributes. Our extensive evaluation highlights that when only a holistic representation is learned, we consistently outperform the state-of-the-art on the three most challenging datasets. Such representations are easier to deploy in practical systems. Finally, we found that joining the losses removes the requirement for having a margin in the softmax loss while increasing performance.

基于深度学习的人重新识别（REID）通常需要大量的培训数据来实现良好的性能。因此，似乎从各种环境中收集更多培训数据往往会提高Reid性能。本文重新审视了这种共同的信念，并使一些令人惊讶的观察结果：使用更多样本，即使用来自多个数据集的样本的培训，不一定通过使用流行的Reid模型来实现更好的性能。在某些情况下，使用更多样本的培训甚至可能损害评估的性能在其中一个数据集中进行。我们假设这一现象是由于标准网络在适应不同环境中的无法行动。为了克服这个问题，我们提出了一种称为域相机样动态网络（DCSD）的方法，其参数可以适应各种因素。具体而言，我们考虑可以从输入特征和外部域相关因子中识别的内部域相关因素，例如域信息或相机信息。我们的发现是，具有这种自适应模型的培训可以从更多的训练样本中获益。实验结果表明，我们的DCSD可以大大提高性能（高达12.3％），而在多个数据集中的联合培训。

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。

物体重新识别（REID）是生物识别和监视系统中最重要的问题之一，在过去几十年来通过图像处理和计算机视觉社区进行了广泛的研究。学习强大而判别的特征表示是对象REID的关键挑战。在REID中，基于无人机（UAV）的REID更具挑战性，因为图像的特征是飞行无人机的摄像机参数（例如，视角，海拔等）的连续变化。为了应对这一挑战，已经考虑了多尺度特征表示形式来表征来自不同海拔无人机飞行的图像。在这项工作中，我们提出了一种多任务学习方法，该方法采用新的多尺度体系结构，无卷积，金字塔视觉变压器（PVT），作为基于无人机的对象REID的骨干。通过对类内变化的不确定性建模，我们提出的模型可以使用不确定性感知对象ID和相机ID信息共同优化。实验结果报告了Prai和VRAI，这是两个REID数据集，从空中监视中验证我们提出的方法的有效性

许多现有人员的重新识别（RE-ID）方法取决于特征图，这些特征图可以分区以定位一个人的部分或减少以创建全球表示形式。尽管部分定位已显示出显着的成功，但它使用了基于位置的分区或静态特征模板。但是，这些假设假设零件在给定图像或其位置中的先前存在，忽略了特定于图像的信息，这些信息限制了其在挑战性场景中的可用性，例如用部分遮挡和部分探针图像进行重新添加。在本文中，我们介绍了一个基于空间注意力的动态零件模板初始化模块，该模块在主链的早期层中使用中级语义特征动态生成零件序列。遵循自发注意力的层，使用简化的跨注意方案来使用主链的人体部分特征来提取各种人体部位的模板特征，提高整个模型的判别能力。我们进一步探索零件描述符的自适应加权，以量化局部属性的缺失或阻塞，并抑制相应零件描述子对匹配标准的贡献。关于整体，遮挡和部分重新ID任务基准的广泛实验表明，我们提出的架构能够实现竞争性能。代码将包含在补充材料中，并将公开提供。

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。

感谢您的跨模式检索技术，通过将它们投射到一个共同的空间中，可以在24小时的监视系统中重新进行重新识别，从而实现了可见的信号（RGB-IR）重新识别（RE-ID）。但是，关于探测到探测器，几乎所有现有的基于RGB-IR的跨模式人RE-ID方法都集中在图像到图像匹配上，而视频对视频匹配包含更丰富的空间 - 和时间信息仍未探索。在本文中，我们主要研究基于视频的跨模式人Re-ID方法。为了实现这项任务，构建了一个基于视频的RGB-IR数据集，其中927个有效身份，具有463,259帧和21,863个曲目，由12个RGB/IR摄像机捕获。基于我们构造的数据集，我们证明，随着曲目中帧的增加，该性能确实达到了更多的增强功能，证明了视频对视频匹配在RGB-IR RE-ID中的重要性。此外，进一步提出了一种新颖的方法，不仅将两种模态投射到模态不变子空间，而且还提取了运动不变的时间记忆。多亏了这两种策略，我们基于视频的跨模式人重新ID取得了更好的结果。代码和数据集以：https：//github.com/vcmproject233/mitml发布。

由于无人驾驶航空公司（无人机）随着越来越多的应用程序变得越来越多，因此无人机中断的潜在风险增加。深度学习的最新发展允许基于视觉的柜台 - 无人机系统用单个相机检测和跟踪无人机。然而，单个摄像机的覆盖范围是有限的，需要需要多色配置以跨越电机匹配UAV - 一个称为重新识别（Reid）的问题。虽然对人和车辆REID已经进行了广泛的研究，以争取时间和观点，但据我们所知，在我们的知识中，在无人机雷德没有研究。无人机挑战重新识别：它们比行人和车辆要小得多，并且它们通常在空气中检测到，所以出现在更大范围内的角度。由于没有UAV数据集目前使用多个摄像机，因此我们提出了第一个新的UAV重新识别数据集，无人机REID，这有助于在该新兴区域开发机器学习解决方案。 UAV-REID有两个设置：临时靠近评估视图的性能，以帮助跟踪框架，并且大到小，以评估跨越规模的REID性能，并在从长途距离检测到无人机时允许早期的REID。我们通过广泛评估不同的REID骨干和损失功能来进行基准研究。我们证明，通过正确的设置，深度网络足够强大，以了解无人机的良好陈述，在时间近的环境中实现81.9％的地图，并在挑战大到小的环境下实现46.5％。此外，我们发现视觉变形金刚是最强大的尺度方差。

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are well suited to represent partially visible human bodies. However, training a part-based model is a challenging task for two reasons. Firstly, individual body part appearance is not as discriminative as global appearance (two distinct IDs might have the same local appearance), this means standard ReID training objectives using identity labels are not adapted to local feature learning. Secondly, ReID datasets are not provided with human topographical annotations. In this work, we propose BPBreID, a body part-based ReID model for solving the above issues. We first design two modules for predicting body part attention maps and producing body part-based features of the ReID target. We then propose GiLt, a novel training scheme for learning part-based representations that is robust to occlusions and non-discriminative local appearance. Extensive experiments on popular holistic and occluded datasets show the effectiveness of our proposed method, which outperforms state-of-the-art methods by 0.7% mAP and 5.6% rank-1 accuracy on the challenging Occluded-Duke dataset. Our code is available at https://github.com/VlSomers/bpbreid.

Object re-identification method is made up of backbone network, feature aggregation, and loss function. However, most backbone networks lack a special mechanism to handle rich scale variations and mine discriminative feature representations. In this paper, we firstly design a hierarchical similarity graph module (HSGM) to reduce the conflict of backbone and re-identification networks. The designed HSGM builds a rich hierarchical graph to mine the mapping relationships between global-local and local-local. Secondly, we divide the feature map along with the spatial and channel directions in each hierarchical graph. The HSGM applies the spatial features and channel features extracted from different locations as nodes, respectively, and utilizes the similarity scores between nodes to construct spatial and channel similarity graphs. During the learning process of HSGM, we utilize a learnable parameter to re-optimize the importance of each position, as well as evaluate the correlation between different nodes. Thirdly, we develop a novel hierarchical similarity graph network (HSGNet) by embedding the HSGM in the backbone network. Furthermore, HSGM can be easily embedded into backbone networks of any depth to improve object re-identification ability. Finally, extensive experiments on three large-scale object datasets demonstrate that the proposed HSGNet is superior to state-of-the-art object re-identification approaches.

Existing person re-identification (re-id) methods either assume the availability of well-aligned person bounding box images as model input or rely on constrained attention selection mechanisms to calibrate misaligned images. They are therefore sub-optimal for re-id matching in arbitrarily aligned person images potentially with large human pose variations and unconstrained auto-detection errors. In this work, we show the advantages of jointly learning attention selection and feature representation in a Convolutional Neural Network (CNN) by maximising the complementary information of different levels of visual attention subject to re-id discriminative learning constraints. Specifically, we formulate a novel Harmonious Attention CNN (HA-CNN) model for joint learning of soft pixel attention and hard regional attention along with simultaneous optimisation of feature representations, dedicated to optimise person re-id in uncontrolled (misaligned) images. Extensive comparative evaluations validate the superiority of this new HA-CNN model for person re-id over a wide variety of state-ofthe-art methods on three large-scale benchmarks including CUHK03, Market-1501, and DukeMTMC-ReID.

In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to use temporal information, which often leads to networks being fooled by similar appearances and same backgrounds. In this paper, we propose a Disentanglement and Switching and Aggregation Network (DSANet), which segregates the features representing identity and features based on camera characteristics, and pays more attention to ID information. We also introduce an auxiliary task that utilizes a new pair of features created through switching and aggregation to increase the network's capability for various camera scenarios. Furthermore, we devise a Target Localization Module (TLM) that extracts robust features against a change in the position of the target according to the frame flow and a Frame Weight Generation (FWG) that reflects temporal information in the final representation. Various loss functions for disentanglement learning are designed so that each component of the network can cooperate while satisfactorily performing its own role. Quantitative and qualitative results from extensive experiments demonstrate the superiority of DSANet over state-of-the-art methods on three benchmark datasets.

Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild.