Vehicle Re-identification Research Articles

MP-GIEN: Vehicle Re-Identification Method Based on Multi-View Progressive Graph Interactive Embedding Network

Vehicle re-identification primarily faces two challenges: significant intra-class variations caused by different camera views and subtle inter-class differences between vehicles of the same model. In this paper, we propose a Multi-View Progressive Graph Interaction Embedding Network (MP-GIEN) for vehicle re-identification. First, we design a global semantic extractor. By employing Graph Interaction Units (GI Units) and semantic context, we combine semantic information to facilitate contextual reasoning of image regions, thereby extracting global semantic features of the target. The global loss is then calculated using ID loss and triplet loss. Second, we devise a local view-aware extractor. The U-Net network is utilized to parse the image into four views (front, rear, top, and side). Subsequently, features are aligned through mask average pooling, and a progressive training strategy is adopted to address the learning of fine-grained information. Lastly, we design a feature fusion enhancer. We revise the typical triplet loss to avoid mismatches in local features. By optimizing the local triplet loss and global loss, we learn the embedding of visual features, which not only reduces the intra-instance distance but also enlarges the inter-instance differences. MP-GIEN facilitates the capture of stable discriminative information on vehicles under different views. Experiments conducted on the VeRi-Wild dataset demonstrate that our model significantly outperforms state-of-the-art methods.

Diversity-Representativeness Replay and Knowledge Alignment for Lifelong Vehicle Re-identification

Lifelong vehicle re-identification (LVReID) aims to match a target vehicle across multiple cameras, considering non-stationary and continuous data streams, which fits the needs of the practical application better than traditional vehicle re-identification. Nonetheless, this area has received relatively little attention. Recently, methods for lifelong person re-identification (LPReID) have been emerging, with replay-based methods achieving the best results by storing a small number of instances from previous tasks for retraining, thus effectively reducing catastrophic forgetting. However, these methods cannot be directly applied to LVReID because they fail to simultaneously consider the diversity and representativeness of replayed data, resulting in biases between the subset stored in the memory buffer and the original data. They randomly sample classes, which may not adequately represent the distribution of the original data. Additionally, these methods fail to consider the rich variation in instances of the same vehicle class due to factors such as vehicle orientation and lighting conditions. Therefore, preserving more informative classes and instances for replay helps maintain information from previous tasks and may mitigate the model’s forgetting of old knowledge. In view of this, we propose a novel Diversity-Representativeness Dual-Stage Sampling Replay (DDSR) strategy for LVReID that constructs an effective memory buffer through two stages, i.e. , Cluster-Centric Class Selection and Diverse Instance Mining. Specifically, we first perform class-level sampling based on density in the clustered class-centered feature space and then further mine the diverse, high-quality instances within the selected classes. In addition, we introduce Maximum Mean Discrepancy loss to align the feature distribution between replay data and the new arrivals and apply L2 regularisation in the parameter space to facilitate knowledge transfer, thus enhancing the model’s generalization ability to new tasks. Extensive experiments demonstrate effective improvements of our method compared to current state-of-the-art lifelong ReID methods on the VeRi-776, VehicleID, and VERI-Wild datasets.

Learning super-resolution and pyramidal convolution residual network for vehicle re-identification

Vehicle re-identification (Vehicle Re-ID) aims at retrieving and tracking the specified target vehicle with multiple other cameras, which can provide help in checking violations and catching fugitives, but there are still the following problems that need to be solved urgently. First, the existing collected Vehicle Re-ID data often have low resolution and blur in local regions, so that the Vehicle Re-ID algorithm cannot accurately extract subtle feature representations. In addition, small features are easy to cause the disappearance of features under the operation of a large convolution kernel, which makes the model unable to capture and learn subtle features, resulting in inaccurate judgment of vehicles. In this study, we propose a Vehicle Re-ID method based on super resolution and pyramidal convolution residual network. Firstly, a super-resolution image generation network leveraging generative adversarial networks (GANs) is proposed. This network employs both content loss and adversarial loss as optimization criteria, ensuring an efficient transformation from a low-resolution image into a super-resolution counterpart, while meticulously preserving intricate high-frequency details. Then, multi levels of pyramidal convolution operations are designed to generate multi-scale features, which can capture information on different scales. Moreover, the concept of residual learning is applied between the multi levels of pyramidal convolution operations to expedite model optimization and enhance recognition capabilities. Ultimately, the double pyramidal convolutions are meticulously employed on both the original image and the super-resolution image, yielding low-noise feature representations and intricate semantic information respectively. By seamlessly fusing these two diverse sources of information, the resultant combined features exhibit heightened discrimination capabilities and significantly bolster the robustness of image features. In order to verify the effectiveness of the proposed method, extensive experiments are carried out on VeRi-776 and VehicleID datasets. The experimental results show that the method proposed in this paper effectively captures the detail information of vehicle images, accurately distinguishes the subtle differences between different vehicles of the same type, and is superior to state-of-the-art methods.

Weakly Supervised Contrastive Learning for Unsupervised Vehicle Reidentification.

Reidentification (Re-id) of vehicles in a multicamera system is an essential process for traffic control automation. Previously, there have been efforts to reidentify vehicles based on shots of images with identity (id) labels, where the model training relies on the quality and quantity of the labels. However, labeling vehicle ids is a labor-intensive procedure. Instead of relying on expensive labels, we propose to exploit camera and tracklet ids that are automatically obtainable during a Re-id dataset construction. In this article, we present weakly supervised contrastive learning (WSCL) and domain adaptation (DA) techniques using camera and tracklet ids for unsupervised vehicle Re-id. We define each camera id as a subdomain and tracklet id as a label of a vehicle within each subdomain, i.e., weak label in the Re-id scenario. Within each subdomain, contrastive learning using tracklet ids is applied to learn a representation of vehicles. Then, DA is performed to match vehicle ids across the subdomains. We demonstrate the effectiveness of our method for unsupervised vehicle Re-id using various benchmarks. Experimental results show that the proposed method outperforms the recent state-of-the-art unsupervised Re-id methods. The source code is publicly available on https://github.com/andreYoo/WSCL_VeReid.

Multi-Target Vehicle Tracking Algorithm Based on Improved DeepSORT.

In this paper, we address the issues of insufficient accuracy and frequent identity switching in the multi-target tracking algorithm DeepSORT by proposing two improvement strategies. First, we optimize the appearance feature extraction process by training a lightweight appearance extraction network (OSNet) on a vehicle re-identification dataset. This makes the appearance features better suited for the vehicle tracking model required in our paper. Second, we improve the metric of motion features by using the original IOU distance metric or GIOU metrics. The optimized tracking algorithm using GIOU achieves effective improvements in tracking precision and accuracy. The experimental results show that the improved vehicle tracking models MOTA and IDF1 are enhanced by 4.6% and 5.9%, respectively. This allows for the stable tracking of vehicles and reduces the occurrence of identity switching phenomenon to a certain extent.

Advances in vehicle re-identification techniques: A survey

The development of vehicle re-identification technology has significantly enhanced the operational efficiency of intelligent transportation systems and smart cities, attributed to the advancement of artificial intelligence technologies such as deep learning and transformer models. By accurately tracking and identifying the same vehicle under different cameras, the technology not only greatly enhances the ability of urban safety monitoring, traffic management and accident investigation, but also provides powerful technical support for the development of intelligent transportation. This paper explores the shift from traditional to deep learning approaches in vehicle re-identification, highlighting the rise of Transformer models. We assess both non-visual and vision-based re-identification technologies, with a special focus on the deep feature-based methods across supervised, unsupervised, and semi-supervised learning. And we summarize the performance of supervised and unsupervised methods on the VeRi-776 and VehicleID datasets. Finally, this paper outlines six directions for the future development of vehicle Re-ID technology, highlighting its potential applications in various areas such as smart city traffic management.

Identifying Re-identification Challenges: Past, Current and Future Trends

Person and vehicle re-identification has been a popular subject in the field of the computer vision technologies. Existing closed-set re-identification surpasses human-level accuracies on commonly used benchmarks, and the research focus for re-identification is shifting to the open world-setting. The latter setting is more suitable for practical applications, however, is less developed due to its challenges. On the other hand, existing research is more focused on person re-identification, even though both, person and vehicle, are important components for smart city applications. This review attempts to combine for the first time the problem of person and vehicle re-identification under closed and open settings, its challenges, and the existing research. Specifically, we start from the origin of the re-identification task and then summarize state-of-the-art research based on deep learning in different scenarios: person or vehicle or unified re-identification in closed- and open-world settings. Additionally, we analyse a new method for solving the re-identification task using the Transformer, a model architecture that relies entirely on an attention mechanism, which shows promising results. This survey facilitates future research by providing a summary on past and present trends, and aids to improve the usability of re-ID techniques.

Camera-Aware Differentiated Clustering With Focal Contrastive Learning for Unsupervised Vehicle Re-Identification

Camera-Aware Differentiated Clustering With Focal Contrastive Learning for Unsupervised Vehicle Re-Identification

Triplet Contrastive Representation Learning for Unsupervised Vehicle Re-identification

Part feature learning plays a crucial role in achieving fine-grained semantic understanding in unsupervised vehicle re-identification. However, existing approaches directly model part and global features, which can easily lead to severe gradient vanishing issues due to their unequal feature information and unreliable pseudo-labels. To address this problem, in this paper, we propose a triplet contrastive representation learning (TCRL) framework, which leverages cluster features to bridge the part features and global features for unsupervised vehicle re-identification. Specifically, TCRL devises three memory banks to store the instance/cluster features and proposes a proxy contrastive loss (PCL) to make contrastive learning between adjacent memory banks, thus presenting the associations between the part and global features as a transition of the part-cluster and cluster-global associations. Since the cluster memory bank copes with all the vehicle features, it can summarize them into a discriminative feature representation. To deeply exploit the instance/cluster information, TCRL proposes two additional loss functions. For the instance-level feature, a hybrid contrastive loss (HCL) re-defines the sample correlations by approaching the positive instance features and pushing all negative instance features away. For the cluster-level feature, a weighted regularization cluster contrastive loss (WRCCL) refines the pseudo labels by penalizing the mislabeled images according to the instance similarity. Extensive experiments show that TCRL outperforms many state-of-the-art unsupervised vehicle re-identification approaches.

Multi-Branch Feature Learning Network via Global-Local Self-Distillation for Vehicle Re-Identification

Multi-Branch Feature Learning Network via Global-Local Self-Distillation for Vehicle Re-Identification

SAM-driven MAE pre-training and background-aware meta-learning for unsupervised vehicle re-identification

Distinguishing identity-unrelated background information from discriminative identity information poses a challenge in unsupervised vehicle re-identification (Re-ID). Re-ID models suffer from varying degrees of background interference caused by continuous scene variations. The recently proposed segment anything model (SAM) has demonstrated exceptional performance in zero-shot segmentation tasks. The combination of SAM and vehicle Re-ID models can achieve efficient separation of vehicle identity and background information. This paper proposes a method that combines SAM-driven mask autoencoder (MAE) pre-training and background-aware meta-learning for unsupervised vehicle Re-ID. The method consists of three sub-modules. First, the segmentation capacity of SAM is utilized to separate the vehicle identity region from the background. SAM cannot be robustly employed in exceptional situations, such as those with ambiguity or occlusion. Thus, in vehicle Re-ID downstream tasks, a spatially-constrained vehicle background segmentation method is presented to obtain accurate background segmentation results. Second, SAM-driven MAE pre-training utilizes the aforementioned segmentation results to select patches belonging to the vehicle and to mask other patches, allowing MAE to learn identity-sensitive features in a self-supervised manner. Finally, we present a background-aware meta-learning method to fit varying degrees of background interference in different scenarios by combining different background region ratios. Our experiments demonstrate that the proposed method has state-of-the-art performance in reducing background interference variations.

Efficient Vehicle-Infrastructure Collaborative Perception Based on Vehicle Re-Identification and Mini-ICP Algorithm

Efficient Vehicle-Infrastructure Collaborative Perception Based on Vehicle Re-Identification and Mini-ICP Algorithm

Text-to-Image Vehicle Re-Identification: Multi-Scale Multi-View Cross-Modal Alignment Network and a Unified Benchmark

Text-to-Image Vehicle Re-Identification: Multi-Scale Multi-View Cross-Modal Alignment Network and a Unified Benchmark

RANDnet: Vehicle Re-Identification with Relation Attention and Nuance–Disparity Masks

RANDnet: Vehicle Re-Identification with Relation Attention and Nuance–Disparity Masks

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.

Stripe-Assisted Global Transformer and Spatial–Temporal Enhancement for Vehicle Re-Identification

As a core technology in intelligent transportation systems, vehicle re-identification has attracted growing attention. Most existing methods use CNNs to extract global and local features from vehicle images and roughly integrate them for identifying vehicles, addressing intra-class similarity and inter-class difference. However, a significant challenge arises from redundant information between global and local features and possible misalignment among local features, resulting in suboptimal efficiency when combined. To further improve vehicle re-identification, we propose a stripe-assisted global transformer (SaGT) method, which leverages a dual-branch network based on transformers to learn a discriminative whole representation for each vehicle image. Specifically, one branch exploits a standard transformer layer to extract a global feature, while the other branch employs a stripe feature module (SFM) to construct stripe-based features. To further facilitate the effective incorporation of local information into the learning process of the global feature, we introduce a novel stripe-assisted global loss (SaGL), which combines ID losses to optimize the model. Considering redundancy, we only use the global feature for inference, as we enhance the whole representation with stripe-specific details. Finally, we introduce a spatial-temporal probability (STPro) to provide a complementary metric for robust vehicle re-identification. Extensive and comprehensive evaluations on two public datasets validate the effectiveness and superiority of our proposed method.

Discriminative Feature Learning With Co-Occurrence Attention Network for Vehicle ReID

Discriminative Feature Learning With Co-Occurrence Attention Network for Vehicle ReID

Drone-captured vehicle re-identification via perspective mask segmentation and hard sample learning

Drone-captured vehicle re-identification via perspective mask segmentation and hard sample learning

TANet: Text region attention learning for vehicle re-identification

In recent years, the challenge of distinguishing vehicles of the same model has prompted a shift towards leveraging both global appearances and local features, such as lighting and rearview mirrors, for vehicle re-identification (ReID). Despite advancements, accurately identifying vehicles remains complex, particularly due to the underutilization of highly discriminative text regions. This paper introduces the Text Region Attention Network (TANet), a novel approach that integrates global and local information with a specific focus on text regions for improved feature learning. TANet uniquely captures stable and distinctive features across various vehicle views, demonstrating its effectiveness through rigorous evaluation on the VeRi-776, VehicleID, and VERI-Wild datasets. TANet significantly outperforms existing methods, achieving mAP scores of 83.6% on VeRi-776, 84.4% on VehicleID (Large), and 76.6% on VERI-Wild (Large). Statistical tests further validate the superiority of TANet over the baseline, showcasing notable improvements in mAP and Top-1 through Top-15 accuracy metrics.