Vehicle Re-identification Methods Research Articles

Vehicle Re-Identification Method Based on Multi-Task Learning in Foggy Scenarios

Vehicle Re-Identification Method Based on Multi-Task Learning in Foggy Scenarios

Vision-language constraint graph representation learning for unsupervised vehicle re-identification

Existing vehicle re-identification methods rely on visual features to extract vehicle identity information. However, while individual visual features enable the model to learn limited semantic information, multimodal representations are difficult to extract. A vision-language constraint graph representation learning method guided by textual descriptions is proposed to exploit the cross-modal robustness capacity. Initially, the training set creates the unique conditional prompts for textual feature extraction. These prompts are employed to improve the understanding of visual modalities. We subsequently designed a vision-language constraint graph topology, where each training sample is considered a node in the graph. Under the dual constraints of visual and textual features, the relationship between graph nodes is further explored to construct more reliable positive and negative sample pairs for graph representation learning. Then, neighboring node label smoothing is introduced to mitigate label noise generated by visual feature clustering and achieved by combining pseudo-label assignment results from neighboring node pairs in the graph topology. Extensive experiments have confirmed that the proposed method achieves state-of-the-art performance by combining salient information from visual and textual modalities.

Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification

The definition of vehicle viewpoint annotations is ambiguous due to human subjective judgment, which makes the cross-domain vehicle re-identification methods unable to learn the viewpoint invariance features during source domain pre-training. This will further lead to cross-view misalignment in downstream target domain tasks. To solve the above challenges, this paper presents a dual-level viewpoint-learning framework that contains an angle invariance pre-training method and a meta-orientation adaptation learning strategy. The dual-level viewpoint-annotation proposal is first designed to concretely redefine the vehicle viewpoint from two aspects (i.e., angle-level and orientation-level). An angle invariance pre-training method is then proposed to preserve identity similarity and difference across the cross-view; this consists of a part-level pyramidal network and an angle bias metric loss. Under the supervision of angle bias metric loss, the part-level pyramidal network, as the backbone, learns the subtle differences of vehicles from different angle-level viewpoints. Finally, a meta-orientation adaptation learning strategy is designed to extend the generalization ability of the re-identification model to the unseen orientation-level viewpoints. Simultaneously, the proposed meta-learning strategy enforces meta-orientation training and meta-orientation testing according to the orientation-level viewpoints in the target domain. Extensive experiments on public vehicle re-identification datasets demonstrate that the proposed method combines the redefined dual-level viewpoint-information and significantly outperforms other state-of-the-art methods in alleviating viewpoint variations.

VERI-D: A new dataset and method for multi-camera vehicle re-identification of damaged cars under varying lighting conditions

Vehicle re-identification (V-ReID) is a critical task that aims to match the same vehicle across images from different camera viewpoints. The previous studies have leveraged attribute clues, such as color, model, and license plate, to enhance the V-ReID performance. However, these methods often lack effective interaction between the global–local features and the final V-ReID objective. Moreover, they do not address the challenging issues in real-world scenarios, such as high viewpoint variations, extreme illumination conditions, and car appearance changes (e.g., due to damage or wrong driving). We propose a novel framework to tackle these problems and advance the research in V-ReID, which can handle various types of car appearance changes and achieve robust V-ReID under varying lighting conditions. Our main contributions are as follows: (i) we propose a new Re-ID architecture named global–local self-attention network, which integrates local information into the feature learning process and enhances the feature representation for V-ReID and (ii) we introduce a novel damaged vehicle Re-ID dataset called VERI-D, which is the first publicly available dataset that focuses on this challenging yet practical scenario. The dataset contains both natural and synthetic images of damaged vehicles captured from multiple camera viewpoints and under different lighting conditions. (iii) We conduct extensive experiments on the VERI-D dataset and demonstrate the effectiveness of our approach in addressing the challenges associated with damaged vehicle re-identification. We also compare our method to several state-of-the-art V-ReID methods and show its superiority.

Vehicle re-identification method based on multi-attribute dense linking network combined with distance control module.

Vehicle re-identification is a crucial task in intelligent transportation systems, presenting enduring challenges. The primary challenge involves the inefficiency of vehicle re-identification, necessitating substantial time for recognition within extensive datasets. A secondary challenge arises from notable image variations of the same vehicle due to differing shooting angles, lighting conditions, and diverse camera equipment, leading to reduced accuracy. This paper aims to enhance vehicle re-identification performance by proficiently extracting color and category information using a multi-attribute dense connection network, complemented by a distance control module. We propose an integrated vehicle re-identification approach that combines a multi-attribute dense connection network with a distance control module. By merging a multi-attribute dense connection network that encompasses vehicle HSV color attributes and type attributes, we improve classification rates. The integration of the distance control module widens inter-class distances, diminishes intra-class distances, and boosts vehicle re-identification accuracy. To validate the feasibility of our approach, we conducted experiments using multiple vehicle re-identification datasets. We measured various quantitative metrics, including accuracy, mean average precision, and rank-n. Experimental results indicate a significant enhancement in the performance of our method in vehicle re-identification tasks. The findings of this study provide valuable insights into the application of multi-attribute neural networks and deep learning in the field of vehicle re-identification. By effectively extracting color information from the HSV color space and vehicle category information using a multi-attribute dense connection network, coupled with the utilization of a distance control module to process vehicle features, our approach demonstrates improved performance in vehicle re-identification tasks, contributing to the advancement of smart city systems.

Camera Topology Graph Guided Vehicle Re-Identification

Vehicle re-identification (Re-ID) aims to retrieve vehicles across non-overlapping cameras. Most studies consider representation learning from single appearance information of the vehicle images. Some works adopt the spatio-temporal information to remove unreasonable vehicles to refine the results in the testing phase. However, they ignore the potential topological relations among cameras under the Closed Circuit Television (CCTV) camera systems in the training phase, which usually leads to suboptimal results due to the high intra-identity variations. To handle this problem, we propose a novel vehicle re-identification framework, which explicitly models the camera topological relations of all input images to aggregate neighbor images and thus acquires camera-independent representations. Specifically, we first construct a Camera Topology Graph (CTG) to elucidate the topological relations among cameras. It takes different cameras as nodes and constructs edges from four levels of the camera system, position, orientation, and individual. Then, we introduce a Camera Topology-based Graph Convolutional Network (CT-GCN), which suppresses irrelevant neighbor images and learns different camera representation functions. Finally, we propose a topological cross-entropy loss to obtain the more discriminative vehicle representations. The whole network is trained in an end-to-end manner. Extensive experiments on three benchmark datasets demonstrate the effectiveness of the proposed method against state-of-the-art vehicle Re-ID methods.

Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer

The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a source-free domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the “source-like samples”. Then, it can effectively reduce the model knowledge difference and improve the model’s generalization performance. In the experiment, two mainstream public datasets in this field, VeRi776 and VehicleID, are tested experimentally, and the obtained rank-k (the cumulative matching features) and mAP (the mean Average Precision) indicators are both improved, which are suitable for object re-identification tasks when data between domains cannot be interoperated.

URRNet: A Unified Relational Reasoning Network for Vehicle Re-Identification

With the continuous improvement and optimization of security monitoring networks, vehicle Re-Identification (Re-ID) becomes an emerging key technology in the development of intelligent visual surveillance systems. Due to the influence of viewpoint variation and fine-grained differences, vehicle Re-ID is still a research topic worth investigating. To alleviate above problems, a novel end-to-end framework named Unified Relational Reasoning Network (URRNet) is proposed in this paper, which integrates global features with local features to obtain better recognition accuracy. For the proposed framework, to understand the overall semantics of the image, an algorithm based on the global feature graph-structure learning is designed. The pixel-level feature maps are transformed to the node features of graph in the interactive space by projection, then graph reasoning is performed by using the graph convolutional network to improve the representation of global features. Moreover, an algorithm based on multi-scale local feature relational reasoning is designed. Using keypoint and viewpoint to obtain the multi-scale partial characteristics of the vehicle, and the vehicle multi-view features are learned from the single-view vehicle images through relational reasoning and attention mechanism. The two algorithms are combined to obtain the overall model, which not only preserves the details of the vehicle, but also effectively solves the problem of viewpoint variation. Comprehensive experimental results on two public datasets (VeRi-776 and VehicleID) indicate that the proposed URRNet can practically improve the model's representation ability and generalization ability, which is comparable to the state-of-the-art vehicle Re-ID methods.

Unsupervised Vehicle Re-Identification Based on Cross-Style Semi-Supervised Pre-Training and Feature Cross-Division

Vehicle Re-Identification (Re-ID) based on Unsupervised Domain Adaptation (UDA) has shown promising performance. However, two main issues still exist: (1) existing methods that use Generative Adversarial Networks (GANs) for domain gap alleviation combine supervised learning with hard labels of the source domain, resulting in a mismatch between style transfer data and hard labels; (2) pseudo label assignment in the fine-tuning stage is solely determined by similarity measures of global features using clustering algorithms, leading to inevitable label noise in generated pseudo labels. To tackle these issues, this paper proposes an unsupervised vehicle re-identification framework based on cross-style semi-supervised pre-training and feature cross-division. The framework consists of two parts: cross-style semi-supervised pre-training (CSP) and feature cross-division (FCD) for model fine-tuning. The CSP module generates style transfer data containing source domain content and target domain style using a style transfer network, and then pre-trains the model in a semi-supervised manner using both source domain and style transfer data. A pseudo-label reassignment strategy is designed to generate soft labels assigned to the style transfer data. The FCD module obtains feature partitions through a novel interactive division to reduce the dependence of pseudo-labels on global features, and the final similarity measurement combines the results of partition features and global features. Experimental results on the VehicleID and VeRi-776 datasets show that the proposed method outperforms existing unsupervised vehicle re-identification methods. Compared with the last best method on each dataset, the method proposed in this paper improves the mAP by 0.63% and the Rank-1 by 0.73% on the three sub-datasets of VehicleID on average, and it improves mAP by 0.9% and Rank-1 by 1% on VeRi-776 dataset.

Coarse-to-fine sparse self-attention for vehicle re-identification

Existing vehicle re-identification (Re-ID) methods usually combine global features and local features to meet the challenge of inter-class similarity and intra-class variance, but almost all existing methods rely on extra auxiliary networks to locate the vehicle parts for feature mining, which are inefficient, cost-ineffective and time-consuming. Self-attention mechanism, averaging model’s attention weights on the similar vehicle parts, can guide the model to identify more discriminative vehicle parts for identification, thus extracting expensive local features becomes unnecessary. However, the computational cost of the original self-attention mechanism is unacceptable and the attention map is less discriminative when processing high-resolution feature maps (i.e. 384*384 or larger), which limits the performance gains of the challenging vehicle Re-ID. In this paper, we propose a lightweight coarse-to-fine sparse self-attention mechanism for vehicle Re-ID. Our method decomposes the self-attention process to a coarse stage and a fine stage. In coarse stage, the pixel-level feature map is transformed to patch-level feature map and the dependencies between similar vehicle parts are captured in a global context. In fine stage, the details of vehicle parts are captured in a local context. In addition, multi-head structure is introduced to capture diverse but robust attention information. Experiments on VeRi and VERI-Wild show that our method not only outperforms recent vehicle Re-ID methods relying on expensive auxiliary networks, but also outperforms the original self-attention mechanism in accuracy with much lower computation and memory cost.

Research on Vehicle Re-Identification Algorithm Based on Fusion Attention Method

The specific task of vehicle re-identification is how to quickly and correctly match the same vehicle in different scenarios. In order to solve the problem of inter-class similarity and environmental interference in vehicle images in complex scenes, one fusion attention method is put forward based on the idea of obtaining the distinguishing features of details—the mechanism for the vehicle re-identification method. First, the vehicle image is preprocessed to restore the image’s attributes better. Then, the processed image is sent to ResNet50 to extract the features of the second and third layers, respectively. Then, the feature fusion is carried out through the two-layer attention mechanism for a network model. This model can better focus on local detail features, and global features are constructed and named SDLAU-Reid. In the training process, a data augmentation strategy of random erasure is adopted to improve the robustness. The experimental results show that the mAP and rank-k indicators of the model on VeRi-776 and the VehicleID are better than the results of the existing vehicle re-identification algorithms, which verifies the algorithm’s effectiveness.

Research on Vehicle Re-identification for Vehicle Road Collaboration

Vehicles and roads cooperate to perceive traffic targets, which can reduce the perception blind spots of vehicles and improve driving safety. In this paper, we proposes a vehicle re-identification method oriented to vehicle-road coordination. This method first designs a lightweight vehicle re-identification network based on ShufflenetV2 to solve the computational efficiency problem of vehicle-road coordination scenarios, which can efficiently complete vehicle feature extraction; then, due to the real-time requirements of scenario communication, an adaptive feature conversion mechanism is designed in combination with the LSH algorithm, which can make the re-identification module to dynamically perform binary bit feature conversion and adjust the dimension according to the communication channel state; finally, a loss function for the conversion of vehicle re-identification features is designed, which can greatly reduce the accuracy loss rate of converting floating-point features to bit features. Experiments show that our method can efficiently complete the information extraction and comparison of vehicle re-identification features in the vehicle-road coordination scenario, and can improve the perception efficiency of vehicle-road coordination while taking into account performance and bandwidth.

Multi-query Vehicle Re-identification: Viewpoint-conditioned Network, Unified Dataset and New Metric.

Existing vehicle re-identification methods mainly rely on the single query, which has limited information for vehicle representation and thus significantly hinders the performance of vehicle Re-ID in complicated surveillance networks. In this paper, we propose a more realistic and easily accessible task, called multi-query vehicle Re-ID, which leverages multiple queries to overcome viewpoint limitation of single one. Based on this task, we make three major contributions. First, we design a novel viewpoint-conditioned network (VCNet), which adaptively combines the complementary information from different vehicle viewpoints, for multi-query vehicle Re-ID. Moreover, to deal with the problem of missing vehicle viewpoints, we propose a cross-view feature recovery module which recovers the features of the missing viewpoints by learnt the correlation between the features of available and missing viewpoints. Second, we create a unified benchmark dataset, taken by 6142 cameras from a real-life transportation surveillance system, with comprehensive viewpoints and large number of crossed scenes of each vehicle for multi-query vehicle Re-ID evaluation. Finally, we design a new evaluation metric, called mean cross-scene precision (mCSP), which measures the ability of cross-scene recognition by suppressing the positive samples with similar viewpoints from the same camera. Comprehensive experiments validate the superiority of the proposed method against other methods, as well as the effectiveness of the designed metric in the evaluation of multi-query vehicle Re-ID. The codes and dataset are available at: https://github.com/zhangchaobin001/VCNet.

Enhanced Vehicle Re-Identification for Smart City Applications Using Zone Specific Surveillance

Vehicle re-identification is an important feature of an intelligent transportation system as part of a smart city application. Vehicle re-identification aims at matching vehicles from images acquired by surveillance cameras at different locations. During rush hours, vehicles are densely occupied across regions such as entry/exit of gated campuses, railways, airports, educational institutions, etc. Due to this uneven flow of traffic, there is a possibility of violation of traffic rules by the vehicles that lead to a security breach. In such scenarios to speed up the re-identification process, it is justified to look into a specific group of surveillance cameras to detect and re-identify vehicles on day to day basis in near real-time. However, the existing vehicle re-identification datasets do not contain zone specific information and therefore can not be used to evaluate the performance of re-identification algorithms in different zones. In the proposed work for re-identification, a framework is developed that performs vehicle re-identification across a group of cameras that monitors varying traffic movements over an area. These areas defined as “strategic zones” comprise a subset of non-overlapping cameras that are installed to monitor non-uniform vehicle movements. The re-identification framework is evaluated on a novel dataset developed to understand the performance of vehicle re-identification across strategic zones. The dataset consists of videos of vehicles captured through 20 CCTV surveillance cameras that are grouped into four different zones. Various experiments are conducted to study the performance of re-identification across four zones using a deep neural network with triplet loss, L2 regularization, and re-ranking. The experiments conducted with an image dimension of 224 × 224 have demonstrated an overall mAP of 77.22%. Also, for each of the four zones a mAP of 82.16%, 69.1%, 66.5%, and 75.76% is achieved. The experimental results demonstrate huge variations in the accuracy of vehicle re-identification method across different zones. Therefore, the study assess the possible measures that can be taken to improve the performance in individual zones for an accurate vehicle re-identification in intelligent transport system.

Vehicle Re-identification method based on Swin-Transformer network

Vehicle Re-identification is to find out the exact vehicle captured by other cameras given a vehicle image. In natural traffic surveillance systems, vehicle re-identification can play a role in target vehicles localization, supervision, and criminal investigation. While current convolutional neural network-based approaches have been very successful, most studies consider learning representations from individual images, ignoring potential interactions between them. The Swin Transformer method currently shows excellent advantages in image classification. It can handle features at different scales like convolutional neural networks and make elements more closely related to each other, enhancing the learning of robust features and improving feature robustness and discrimination compared to convolutional neural nets. Therefore, this paper adopts the Swin Transformer method for vehicle re-identification and conducts experimental validation on two large vehicle datasets, VeRi-776, and VehicleID. The experimental results show that the recognition effect of this method is significantly better than existing vehicle re-identification methods, such as VAM, FDA-Net, TransReID, etc.

Traffic-Informed Multi-Camera Sensing (TIMS) System Based on Vehicle Re-Identification

Surveillance cameras are widely deployed traffic sensors, due to their affordable prices and being able to capture rich information. However, current surveillance systems have not been fully exploited: these cameras are isolated and can only extract information from their own fixed views. To enable a collaborative sensing system, we propose a novel framework called Traffic-Informed Multi-camera Sensing (TIMS) system for network-level traffic information estimation. By pushing multi-camera Re-IDentification (ReID) workflow towards network-wide traffic information extraction, TIMS system integrates a customized metric-learning vision-based vehicle ReID method (TIM-ReID) and establishing a traffic-informed workflow. To integrate the traffic network connection information along with visual and vehicle attributes features, the road network is extracted as a weighted graph through the Spatial-temporal Camera Graph Inference Model (StCGIM) and serves for matching and re-ranking ReID candidates. Moreover, an Accuracy Model (AAM) is designed to provide accurate, reliable and comprehensive traffic information estimation, including both the values and distribution of parameters under a high penetration rate. In experiments based on real-world multi-camera datasets captured in the city of Seattle, the customized TIM-ReID outperforms existing state-of-the-art methods, and delivers accurate cross-camera information estimation, whose value error is less than 8% and the Kullback-Leibler (KL) distance between the estimated and real distribution is less than 3.42 among all the evaluated camera pairs. TIMS system empowers cameras to work collaboratively through an interactive brain, and provides users with valuable and comprehensive traffic information.

Vehicle Re-Identification Based on UAV Viewpoint: Dataset and Method

High-resolution remote sensing images bring a large amount of data as well as challenges to traditional vision tasks. Vehicle re-identification (ReID), as an essential vision task that can utilize remote sensing images, has been widely used in suspect vehicle searches, cross-border vehicle tracking, traffic behavior analysis, and automatic toll collection systems. Although there have been a large number of studies on vehicle ReID, most of them are based on fixed surveillance cameras and do not take full advantage of high-resolution remote sensing images. Compared with images collected by fixed surveillance cameras, high-resolution remote sensing images based on Unmanned Aerial Vehicles (UAVs) have the characteristics of rich viewpoints and a wide range of scale variations. These characteristics bring richer information to vehicle ReID tasks and have the potential to improve the performance of vehicle ReID models. However, to the best of our knowledge, there is a shortage of large open-source datasets for vehicle ReID based on UAV views, which is not conducive to promoting UAV-view-based vehicle ReID research. To address this issue, we construct a large-scale vehicle ReID dataset named VRU (the abbreviation of Vehicle Re-identification based on UAV), which consists of 172,137 images of 15,085 vehicles captured by UAVs, through which each vehicle has multiple images from various viewpoints. Compared with the existing vehicle ReID datasets based on UAVs, the VRU dataset has a larger volume and is fully open-source. Since most of the existing vehicle ReID methods are designed for fixed surveillance cameras, it is difficult for these methods to adapt to UAV-based vehicle ReID images with multi-viewpoint and multi-scale characteristics. Thus, this work proposes a Global Attention and full-Scale Network (GASNet) for the vehicle ReID task based on UAV images. To verify the effectiveness of our GASNet, GASNet is compared with the baseline models on the VRU dataset. The experiment results show that GASNet can achieve 97.45% Rank-1 and 98.51% mAP, which outperforms those baselines by 3.43%/2.08% improvements in terms of Rank-1/mAP. Thus, our major contributions can be summarized as follows: (1) the provision of an open-source UAV-based vehicle ReID dataset, (2) the proposal of a state-of-art model for UAV-based vehicle ReID.

TBE-Net: A Three-Branch Embedding Network With Part-Aware Ability and Feature Complementary Learning for Vehicle Re-Identification

Vehicle re-identification (Re-ID) is one of the promising applications in the field of computer vision. Existing vehicle Re-ID methods mainly focus on global appearance features or pre-defined local region features, which have difficulties in handling inter-class similarities and intra-class differences among vehicles in various traffic scenarios. This paper proposes a novel end-to-end three-branch embedding network (TBE-Net) with feature complementary learning and part-aware ability. The proposed TBE-Net integrates complementary features, global appearance, and local region features into a unified framework for subtle feature learning, thereby obtaining more integral and diverse vehicle features to re-identify the vehicle from similar ones. The local region feature branch in the proposed TBE-Net contains an attention module that highlights the major differences among local regions by adaptively assigning large weights to the critical local regions and small weights to insignificant local regions, thereby enhancing the perception sensitivity of the network to subtle discrepancies. The complementary branch in the proposed TBE-Net exploits different pooling operations to obtain more comprehensive structural features and multi-granularity features as a supplement to the global appearance and local region features. The abundant features help accommodate the ever-changing critical local regions in vehicles’ images due to the sensors’ settings, such as the position and shooting angle of surveillance cameras. The extensive experiments on VehicleID and VeRi-776 datasets show that the proposed TBE-Net outperforms the state-of-the-art methods.

Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-Identification

Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks, such as visual geometry group network (VGGNet), Google network (GoogLeNet) and residual network (ResNet). They ignore exploring the spatial significance of feature maps, eventually degrading the vehicle re-identification performance. In this paper, firstly, an innovative spatial graph network (SGN) is proposed to elaborately explore the spatial significance of feature maps. The SGN stacks multiple spatial graphs (SGs). Each SG assigns feature map’s elements as nodes and utilizes spatial neighborhood relationships to determine edges among nodes. During the SGN’s propagation, each node and its spatial neighbors on an SG are aggregated to the next SG. On the next SG, each aggregated node is re-weighted with a learnable parameter to find the significance at the corresponding location. Secondly, a novel pyramidal graph network (PGN) is designed to comprehensively explore the spatial significance of feature maps at multiple scales. The PGN organizes multiple SGNs in a pyramidal manner and makes each SGN handles feature maps of a specific scale. Finally, a hybrid pyramidal graph network (HPGN) is developed by embedding the PGN behind a ResNet-50 based backbone network. Extensive experiments on three large scale vehicle databases (i.e., VeRi776, VehicleID, and VeRi-Wild) demonstrate that the proposed HPGN is superior to state-of-the-art vehicle re-identification approaches in terms of accuracy, parameter cost, and computation cost. In addition, experiments show that the proposed PGN is universal to various backbone networks.

SJDL-Vehicle: Semi-supervised Joint Defogging Learning for Foggy Vehicle Re-identification

Vehicle re-identification (ReID) has attracted considerable attention in computer vision. Although several methods have been proposed to achieve state-of-the-art performance on this topic, re-identifying vehicle in foggy scenes remains a great challenge due to the degradation of visibility. To our knowledge, this problem is still not well-addressed so far. In this paper, to address this problem, we propose a novel training framework called Semi-supervised Joint Defogging Learning (SJDL) framework. First, the fog removal branch and the re-identification branch are integrated to perform simultaneous training. With the collaborative training scheme, defogged features generated by the defogging branch from input images can be shared to learn better representation for the re-identification branch. However, since the fog-free image of real-world data is intractable, this architecture can only be trained on the synthetic data, which may cause the domain gap problem between real-world and synthetic scenarios. To solve this problem, we design a semi-supervised defogging training scheme that can train two kinds of data alternatively in each iteration. Due to the lack of a dataset specialized for vehicle ReID in the foggy weather, we construct a dataset called FVRID which consists of real-world and synthetic foggy images to train and evaluate the performance. Experimental results show that the proposed method is effective and outperforms other existing vehicle ReID methods in the foggy weather. The code and dataset are available in https://github.com/Cihsaing/SJDL-Foggy-Vehicle-Re-Identification--AAAI2022.