Multiple Non-overlapping Cameras Research Articles

Deep video-based person re-identification (Deep Vid-ReID): comprehensive survey

Person re-identification (ReID) aims to find the person of interest across multiple non-overlapping cameras. It is considered an essential step for person tracking applications which is vital for surveillance. Person ReID could be investigated either using image-based or video-based. Video-based person ReID is considered more discriminating and realistic than image-based ReID due to the massive information extracted for each person. Different deep-learning techniques have been used for video-based ReID. In this survey, recently published articles are reviewed according to video-based ReID system pipeline: deep features learning, deep metric learning, and deep learning approaches. The deep feature learning approaches are categorized into spatial and temporal approaches, while deep metric learning is divided into metric and metric learning approaches. The deep learning approaches are differentiated into: supervised, unsupervised, weakly-supervised, and one-shot learning. A detailed analysis is held for the architectures of the state-of-the-art deep learning approaches. And their performance on four benchmark datasets is compared.

An Improved CycleGAN for Data Augmentation in Person Re-Identification

Person re-identification (ReID) has attracted more and more attention, which is to retrieve interested persons across multiple non-overlapping cameras. Matching the same person between different camera styles has always been an enormous challenge. In the existing work, cross-camera styles images generated by the cycle-consistent generative adversarial network (CycleGAN) only transfer the camera resolution and ambient lighting. The generated images produce considerable redundancy and inappropriate pictures at the same time. Although the data is added to prevent over-fitting, it also makes significant noise, so the accuracy is not significantly improved. In this paper, an improved CycleGAN is proposed to generate images for achieving improved data augmentation. The transfer of pedestrian posture is added at the same time as transferring the image style. It not only increases the diversity of pedestrian posture but also reduces the domain gap caused by the style change between cameras. Besides, through the multi-pseudo regularized label (MpRL), the generated images are assigned virtual tags dynamically in training. Through many experimental evaluations, we have achieved a very high identification accuracy on Market-1501, DukeMTMC-reID, and CUHK03-NP datasets. On the three datasets, the quantitative results of mAP are 96.20%, 93.72%, and 86.65%, and the quantitative results of rank-1 are 98.27%, 95.37%, and 90.71%, respectively. The experimental results fully show the superiority of our proposed method.

TransPose Re-ID: transformers for pose invariant person Re-identification

ABSTRACT Person re-identification (Re-ID) is a computer vision task that involves recognizing and tracking individuals across multiple non-overlapping cameras or over time within the same camera view. It is particularly important in surveillance systems, where it can help in identifying potential threats or tracking suspects. Convolutional neural networks (CNNs) have been used to extract invariant person representation for this challenging task. However, CNNs do not consider global dependencies in their initial layers, causing some vital information to be lost during the convolution process. The development of vision-based transformers has opened up new research avenues for person re-identification. This work proposes a purely transformer-based solution, called TansPose Re-ID, that learns pose-invariant person representations. The proposed system uses a vision transformer baseline and enhances its architecture by introducing multiple streams to learn global and local dependencies as well as pose invariance in person images. The architecture includes a Global Self-Attention Module (GSM) and a Local Self-Attention Module (LSM) that jointly learn global and local patch-based person embeddings. The LSM is further improved by stochastically grouping local patches and aligning them. Additionally, an attention feature learning module (AFLM) is introduced in the LSM to handle pose and viewpoint variations. The proposed method is evaluated on two public Re-ID benchmarks, Market1501 and DukeMTMC-ReID, and demonstrates superior performance compared to existing transformer baselines.

A Review of Techniques on Gait-Based Person Re-Identification

Survey/review study A Review of Techniques on Gait-Based Person Re-Identification Babak Rahi 1,*, Maozhen Li 1, and Man Qi 2 1 Department of Electronics and Computer Engineering, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom 2 The School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom * Correspondence: babak.h.rahi@hotmail.com Received: 16 October 2022 Accepted: 14 December 2022 Published: 27 March 2023 Abstract: Person re-identification at a distance across multiple non-overlapping cameras has been an active research area for years. In the past ten years, short-term Person re-identification techniques have made great strides in accuracy using only appearance features in limited environments. However, massive intra-class variations and inter-class confusion limit their ability to be used in practical applications. Moreover, appearance consistency can only be assumed in a short time span from one camera to the other. Since the holistic appearance will change drastically over days and weeks, the technique, as mentioned above, will be ineffective. Practical applications usually require a long-term solution in which the subject's appearance and clothing might have changed after the elapse of a significant period. Facing these problems, soft biometric features such as Gait has stirred much interest in the past years. Nevertheless, even Gait can vary with illness, ageing and emotional states, walking surfaces, shoe types, clothes types, carried objects (by the subject) and even environment clutters. Therefore, Gait is considered as a temporal cue that could provide biometric motion information. On the other hand, the shape of the human body could be viewed as a spatial signal which can produce valuable information. So extracting discriminative features from both spatial and temporal domains would benefit this research. This article examines the main approaches used in gait analysis for re-identification over the past decade. We identify several relevant dimensions of the problem and provide a taxonomic analysis of current research. We conclude by reviewing the performance levels achievable with current technology and providing a perspective on the most challenging and promising research directions.

Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information

Vehicle re-identification (ReID) aims to find a specific vehicle identity across multiple non-overlapping cameras. The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.

Learning to generalize aerial person re‐identification using the meta‐transfer method

SummaryPerson re‐identification (Re‐ID) aims to retrieve a person of interest across multiple nonoverlapping cameras. In recent years, to enable person Re‐ID technology to play out its application value in real‐world scenarios, visual surveillance through unmanned aerial vehicle (UAV) platforms has received intense attention, and aerial person datasets have been constructed. However, the pedestrian images captured by ground cameras and those captured by UAVs exhibit great differences. Person Re‐ID methods based on ground person images have difficulty performing Re‐ID on aerial person images. In this paper, we first use a meta‐transfer method to learn to generalize the aerial person Re‐ID task. Specifically, combining the ideas of meta‐learning and transfer learning, a meta‐learning strategy is introduced to study a feature extractor, and a transfer learning strategy is introduced to utilize and further improve the acquired meta‐knowledge. To prevent the catastrophic forgetting and overfitting problems caused by large‐scale model parameters, we freeze the lower‐layer neurons with powerful generalization and fine‐tune the higher‐layer neurons with strong specialization to transfer and represent the feature extractor. In addition, during the model training process, it is observed that the presence of difficult categories in the given dataset significantly affects the convergence speed and recognition accuracy of the utilized meta‐learning method, and the loss function based on the general Euclidean distance measure tends to mislead the model to optimize in a suboptimal direction. Therefore, we introduce a curriculum sampling based learning strategy that is harmonized with our meta‐transfer learning framework and a new metric formulation of sample similarity based on the Mahalanobis distance to improve the model. In the experimental part, when our method is adopted, a Rank‐1 accuracy of 63.63% and a mean average precision (mAP) of 38.02% are achieved on an aerial Re‐ID dataset, demonstrating its potential for completing person Re‐ID with aerial images. The results obtained on two commonly used ground pedestrian datasets show the generalization of the proposed method. Ablation studies also validate that each component contributes to improving the performance of the model.

A Double Stream Person Re-Identification Method Based on Attention Mechanism and Multi-Scale Feature Fusion

Person re-identification is the key technology for designing a intelligent video surveillance system to retrieve and recongise a specific person in a series of videos. It mainly plays a role in multiple non-overlapping camera monitoring environments to determine whether the target person of interest that has appeared under a camera appears again under others. However, the image data, in the real scene, taken by the surveillance camera may be occluded or blurred, which increased the difficulty of identifying the pedestrian posturn and then lead to dramatically decrease the accuracy of recognition. To solve the above problems, we propose a dualbranch multi-scale feature fusion network, which improves the expression ability of pedestrian features under partial occlusion by learning discriminative pedestrian features. The feature erasure mechanism is introduced into the branch network to effectively enhance the fine-grained feature learning ability of the network. By embedding the lightweight attention module into Resnet-50, the image sequence features of the channel dimension will be extracted, and the interference caused by the cluttered back ground information will be suppressed. In the training phase, the average pooling layer and the maximum pooling layer of different kernels and strides are utilized for different residual stages, and the mixed pooling strategy and mixed loss function of different kernels are designed. Finally, a novel hybrid loss function which combines triplet loss and ID loss is presented to train the network model. By comparison with the existing representative methods on Market1501, DukeMTMC reID and MTMS17 datasets, the experimental results show that the features extracted by the proposed method are more discriminative and with high recognition accuracy.

Multi-granularity graph pooling for video-based person re-identification

The video-based person re-identification (ReID) aims to identify the given pedestrian video sequence across multiple non-overlapping cameras. To aggregate the temporal and spatial features of the video samples, the graph neural networks (GNNs) are introduced. However, existing graph-based models, like STGCN, perform the mean/max pooling on node features to obtain the graph representation, which neglect the graph topology and node importance. In this paper, we propose the graph pooling network (GPNet) to learn the multi-granularity graph representation for the video retrieval, where the graph pooling layer is implemented to downsample the graph. We construct a multi-granular graph by using node features learned from backbone, then implement multiple graph convolutional layers to perform the spatial and temporal aggregation on nodes. To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods. Specifically, MHFAPool first learns a full attention matrix for each pooled node, then obtains the principal eigenvector of the attention matrix via the power iteration algorithm, finally takes the softmax of the principal eigenvector as the aggregation coefficients. Extensive experiments demonstrate that our GPNet achieves the competitive results on four widely-used datasets, i.e., MARS, DukeMTMC-VideoReID, iLIDS-VID and PRID-2011.

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras

The increase in security threats and a huge demand for smart transportation applications for vehicle identification and tracking with multiple non-overlapping cameras have gained a lot of attention. Moreover, extracting meaningful and semantic vehicle information has become an adventurous task, with frameworks deployed on different domains to scan features independently. Furthermore, approach identification and tracking processes have largely relied on one or two vehicle characteristics. They have managed to achieve a high detection quality rate and accuracy using Inception ResNet and pre-trained models but have had limitations on handling moving vehicle classes and were not suitable for real-time tracking. Additionally, the complexity and diverse characteristics of vehicles made the algorithms impossible to efficiently distinguish and match vehicle tracklets across non-overlapping cameras. Therefore, to disambiguate these features, we propose to implement a Ternion stream deep convolutional neural network (TSDCNN) over non-overlapping cameras and combine all key vehicle features such as shape, license plate number, and optical character recognition (OCR). Then jointly investigate the strategic analysis of visual vehicle information to find and identify vehicles in multiple non-overlapping views of algorithms. As a result, the proposed algorithm improved the recognition quality rate and recorded a remarkable overall performance, outperforming the current online state-of-the-art paradigm by 0.28% and 1.70%, respectively, on vehicle rear view (VRV) and Veri776 datasets.

Multitask Multigranularity Aggregation With Global-Guided Attention for Video Person Re-Identification

The goal of video-based person re-identification (Re-ID) is to identify the same person across multiple non-overlapping cameras. The key to accomplishing this challenging task is to sufficiently exploit both spatial and temporal cues in video sequences. However, most current methods are incapable of accurately locating semantic regions or efficiently filtering discriminative spatio-temporal features; so it is difficult to handle issues such as spatial misalignment and occlusion. Thus, we propose a novel feature aggregation framework, multi-task and multi-granularity aggregation with global-guided attention (MMA-GGA), which aims to adaptively generate more representative spatio-temporal aggregation features. Specifically, we develop a multi-task multi-granularity aggregation (MMA) module to extract features at different locations and scales to identify key semantic-aware regions that are robust to spatial misalignment. Then, to determine the importance of the multi-granular semantic information, we propose a global-guided attention (GGA) mechanism to learn weights based on the global features of the video sequence, allowing our framework to identify stable local features while ignoring occlusions. Therefore, the MMA-GGA framework can efficiently and effectively capture more robust and representative features. Extensive experiments on four benchmark datasets demonstrate that our MMA-GGA framework outperforms current state-of-the-art methods. In particular, our method achieves a rank-1 accuracy of 91.0% on the MARS dataset, the most widely used database, significantly outperforming existing methods.

Feature-Level Camera Style Transfer for Person Re-Identification

The person re-identification (re-ID) problem has attracted growing interest in the computer vision community. Most public re-ID datasets are captured by multiple non-overlapping cameras, and the same person may appear dissimilar in different camera views due to variances of illuminations, viewpoints and postures. These differences, collectively referred to as camera style variance, make person re-ID still a challenging problem. Recently, researchers have attempted to solve this problem using generative models. The generative adversarial network (GAN) is widely used for the pose transfer or data augmentation to bridge the camera style gap. However, these methods, mostly based on image-level GAN, require huge computational power during the training of generative models. Furthermore, the training process of GAN is separated from the re-ID model, which makes it hard to achieve a global optimal for both models simultaneously. In this paper, the authors propose to alleviate camera style variance in the re-ID problem by adopting a feature-level Camera Style Transfer (CST) model, which can serve as an intra-class augmentation method and enhance the model robustness against camera style variance. Specifically, the proposed CST method transfers the camera style-related information of input features while preserving the corresponding identity information. Moreover, the training process can be embedded into the re-ID model in an end-to-end manner, which means the proposed approach can be deployed with much less time and memory cost. The proposed approach is verified on several different person re-ID baselines. Extensive experiments show the validity of the proposed CST model and its benefits for re-ID performance on the Market-1501 dataset.

Selective Multibranch Attention Network With Material Constraint for Baggage Reidentification

Baggage reidentification (re-ID) expects to retrieve the unique baggage across multiple nonoverlapping cameras between the precheck and postcheck areas. This critical challenge task lies in two folds: intraclass dissimilarity by pose variations and interclass similarity due to the identical style. In this article, we solve the mentioned problems by a selective multibranch deep network (SMBNet) architecture with the attention mechanism. Specifically, SMBNet incorporates one branch for global feature representations and multiple-part representations corresponding to different local regions. Meanwhile, we plugged the attention block into the part branch to further help the network capture the dependence of the context on local features. Depending on the class response to each part, we propose an adaptive part selection (APS) to automatically select the appropriate part branch and joint with the global one to build a comprehensive baggage descriptor. It effectively blurs unreliable parts and significantly improves representational performance. Finally, we couple classification supervision and material signal in triplet loss to learn a discriminative embedding space that can compact class and material-invariant features and better separate specific representations. Extensive experiments demonstrate the superiority of SMBNet, which achieves 84.7% mean average precision (mAP) and 82.8% Rank-1 on the MVB dataset and significantly outperforms the state-of-the-art methods.

Learning latent features with local channel drop network for vehicle re-identification

Vehicle re-identification targets to find the target vehicle images in a large dataset which is composed of vehicle images from multiple non-overlapping cameras. Due to the various illumination, viewpoints and resolutions, it is challenging to find the right vehicle images accurately. Most existing works put emphasis on learning strong features by exploiting the attention parts in vehicle images, which leads to some small important cues being suppressed by these significant parts. Hence, a local channel drop network (LCDNet) is proposed in this paper, which focuses on seeking the latent features by releasing the constraint of most attentive features. Specially, besides the normal local feature learning network, LCDNet consists of an attentive local feature learning branch that drops some regions to promote learning the attentive features of local regions. Besides, the batch ranking loss is introduced to split the samples into two groups in a batch and regularize them by enforcing a margin, which ensures the model to learn meaningful features to distinct vehicles. Moreover, to further calculate the similarity of various images, the paper proposes a multi-distance based ranking method to achieve more accurate results. Experiments on several benchmark datasets validate the effectiveness of the proposed method.

Deep Graph Metric Learning for Weakly Supervised Person Re-Identification.

In conventional person re-identification (re-id), the images used for model training in the training probe set and training gallery set are all assumed to be instance-level samples that are manually labeled from raw surveillance video (likely with the assistance of detection) in a frame-by-frame manner. This labeling across multiple non-overlapping camera views from raw video surveillance is expensive and time consuming. To overcome these issues, we consider a weakly supervised person re-id modeling that aims to find the raw video clips where a given target person appears. In our weakly supervised setting, during training, given a sample of a person captured in one camera view, our weakly supervised approach aims to train a re-id model without further instance-level labeling for this person in another camera view. The weak setting refers to matching a target person with an untrimmed gallery video where we only know that the identity appears in the video without the requirement of annotating the identity in any frame of the video during the training procedure. The weakly supervised person re-id is challenging since it not only suffers from the difficulties occurring in conventional person re-id (e.g., visual ambiguity and appearance variations caused by occlusions, pose variations, background clutter, etc.), but more importantly, is also challenged by weakly supervised information because the instance-level labels and the ground-truth locations for person instances (i.e., the ground-truth bounding boxes of person instances) are absent. To solve the weakly supervised person re-id problem, we develop deep graph metric learning (DGML). On the one hand, DGML measures the consistency between intra-video spatial graphs of consecutive frames, where the spatial graph captures neighborhood relationship about the detected person instances in each frame. On the other hand, DGML distinguishes the inter-video spatial graphs captured from different camera views at different sites simultaneously. To further explicitly embed weak supervision into the DGML and solve the weakly supervised person re-id problem, we introduce weakly supervised regularization (WSR), which utilizes multiple weak video-level labels to learn discriminative features by means of a weak identity loss and a cross-video alignment loss. We conduct extensive experiments to demonstrate the feasibility of the weakly supervised person re-id approach and its special cases (e.g., its bag-to-bag extension) and show that the proposed DGML is effective.

Pose-guided part matching network via shrinking and reweighting for occluded person re-identification

Occluded person re-identification (ReID) is a challenging task, which aims at retrieving an occluded person across multiple non-overlapping cameras. To address this issue, we propose a novel framework named Shrinking and Reweighting Network (SRNet) that jointly learns global features by shrinking and reweights part features for matching in an end-to-end framework. Specifically, we use a strong backbone that combines some effective designs and training tricks to learn the robust and discriminative global features. Even so, there exist noise-related features due to the occlusion, so we utilize the Deep Residual Shrinkage Module (DRS Module) to eliminate unimportant features by automatically determining the soft thresholds. When aligning two groups of part features from two images, we view it as a graph matching problem and design an effectively Reweight Module for Part Matching (RMPM) to learn self-adaptive weights for part features before the part matching stage, the proposed RMPM can alleviate the influence of meaningless part features in the part matching stage. Eventually, extensive experimental results on occluded, partial, and holistic re-id datasets clearly demonstrate that the proposed method achieves competitive performance to the state-of-the-art methods. Specifically, our framework remarkably outperforms state-of-the-art by 8.9% mAP scores on Occluded-Duke dataset. Code is available at https://github.com/chenxiangzZ/SRNet.

Person Reidentification via Unsupervised Cross-View Metric Learning.

Person reidentification (Re-ID) aims to match observations of individuals across multiple nonoverlapping camera views. Recently, metric learning-based methods have played important roles in addressing this task. However, metrics are mostly learned in supervised manners, of which the performance relies heavily on the quantity and quality of manual annotations. Meanwhile, metric learning-based algorithms generally project person features into a common subspace, in which the extracted features are shared by all views. However, it may result in information loss since these algorithms neglect the view-specific features. Besides, they assume person samples of different views are taken from the same distribution. Conversely, these samples are more likely to obey different distributions due to view condition changes. To this end, this paper proposes an unsupervised cross-view metric learning method based on the properties of data distributions. Specifically, person samples in each view are taken from a mixture of two distributions: one models common prosperities among camera views and the other focuses on view-specific properties. Based on this, we introduce a shared mapping to explore the shared features. Meanwhile, we construct view-specific mappings to extract and project view-related features into a common subspace. As a result, samples in the transformed subspace follow the same distribution and are equipped with comprehensive representations. In this paper, these mappings are learned in an unsupervised manner by clustering samples in the projected space. Experimental results on five cross-view datasets validate the effectiveness of the proposed method.

Deep Learning for Person Re-Identification: A Survey and Outlook.

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.

Viewpoint Transform Matching model for person re-identification

Person re-identification is a challenging problem that aims at matching persons across multiple non-overlapping cameras. Previous works on person re-identification mainly focus on solving the problems caused by pose variations, backgrounds, and occlusion while ignoring the viewpoint variations. To address the viewpoint problem, we propose a Viewpoint Transform Matching (VTM) model, which reduces the inference of viewpoints by feature-level viewpoint transformation. Overall, we design a framework by using viewpoint specific branches to separately extract the representation of different viewpoints. Specifically, we first select the branch for an input image, according to its viewpoint information. In the branch, we transform the feature to other viewpoints by establishing a mutually transformable connection between the features of different viewpoints. To suppress the interference among viewpoints, we propose a viewpoint transform classifier module to independently train a classifier for each viewpoint. To improve the effectiveness of the transform, we propose a viewpoint transform loss to guarantee the consistency of the original features and the transformed features. Experiments conducted on Market-1501, DukeMTMC-reID and CUHK03 show the effectiveness of the proposed method.

Face recognition and tracking for security surveillance

According to the National Crime Records Bureau, 63,407 children have gone missing in the year 2016, which makes almost 174 children go missing in India every day, out of which only 50% are ever found again. This brings up a need for an efficient solution to trace missing children. The proposed solution uses machine assistance during these search activities with face recognition technologies and can be used for essential development of applications which use CCTV footage across a camera network to identify the person lost. In our solution we use One Shot learning for face recognition to identify stranded people in places such as mass gatherings. The same technology can be used for identification of criminals across the city. The paper also talks about the tracking of people across a network of multiple non-overlapping cameras, with a feature of shifting the target tovehicle, if the target gets into one. The experimentation is performed using mobile cameras and thus, helps in monitoring actions of criminals and finding their hideout.

Exciting-Inhibition Network for Person Reidentification in Internet of Things

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping cameras, has attracted more interest due to its significant and potential application in the Internet of Things like intelligent visual surveillance. However, person reID is still a challenging problem in the situations of various pose, similar appearances, partial occlusion, etc. To handle these obstacles, in this article, we investigate an innovative exciting-inhibition network (EINet) that is a two-branch network composed of the exciting branch and the inhibition branch. The channel-spatial attention block that recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch. A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location is applied in the inhibition branch to inhibit the trivial by an inhibitive mask and reinforce learning the remaining regions. We integrate the comprehensive features from both branches for evaluation and show the performance of EINet intuitively using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the previous methods on three popular person re-ID benchmarks. For example, our method obtains 95.64% Rank-1 and 88.75% mean average precision (mAP) on Market-1501, and 77.00% Rank-1 and 74.51% mAP on CUHK03-Detect in the single query mode, respectively.