Vehicle Re-identification Research Articles

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.

Multi-camera Vehicle Tracking and Recognition with Multimodal Contrastive Domain Sharing GAN and Topological Embeddings

Tracking vehicles across a city using a network of multiple cameras are pivotal for enhancing urban and traffic management systems. However, this task is riddled with challenges such as wide geographical coverage, frequent view obstructions, and the diverse appearances of vehicles from various angles.
 To address these complexities, the proposed solution, dubbed Overlapped Vehicle Detection and Tracking using Multimodal Contrastive Domain Sharing Generative Adversarial Network optimized with Efficient Multi-camera system (MCDS-GAN), leverages cutting-edge techniques from computer vision, image processing, machine learning, and sensor fusion. This advanced system detects and tracks vehicles even in scenarios where multiple camera views overlap, making it applicable across domains like traffic management, surveillance, and autonomous vehicles.
 The methodology involves utilizing datasets like Common Objects in Context and ImageNet for training. Detection and tracking are performed using the Multimodal Contrastive Domain Sharing Generative Adversarial Network, followed by vehicle re-identification facilitated by the Topological Information Embedded Convolution Neural Network (TIE-CNN).
 Moreover, optimization techniques are employed to ensure synchronization and efficiency within the system. Implemented in Python, the effectiveness of MCDS-GAN is rigorously evaluated using metrics such as Accuracy, Precision, Recall, Latency, Response Time, and Scalability. Simulation results showcase its superiority, achieving significantly higher accuracy rates compared to existing methods such as OC-MCT-OFOV, MT-MCT-VM-CLM, and TI-VRI.

Paying Attention to Vehicles: A Systematic Review on Transformer-Based Vehicle Re-Identification

Vehicle re-identification (v-reID) is a crucial and challenging task in the intelligent transportation systems (ITS). While vehicle re-identification plays a role in analysing traffic behaviour, criminal investigation, or automatic toll collection, it is also a key component for the construction of smart cities. With the recent introduction of transformer models and their rapid development in computer vision, vehicle re-identification has also made significant progress in performance and development over 2021-2023. This bite-sized review is the first to summarize existing works in vehicle re-identification using pure transformer models and examine their capabilities. We introduce the various applications and challenges, different datasets, evaluation strategies and loss functions in v-reID. A comparison between existing state-of-the-art methods based on different research areas is then provided. Finally, we discuss possible future research directions and provide a checklist on how to implement a v-reID model. This checklist is useful for an interested researcher or practitioner who is starting their work in this field, and also for anyone who seeks an insight into how to implement an AI model in computer vision using v-reID.

MSFFT: Multi-Scale Feature Fusion Transformer for cross platform vehicle re-identification

A vital component of Intelligent Transportation Systems (ITS) is vehicle re-identification, which allows vehicles to be identified across surveillance devices. Re-identification of vehicles is usually done using information collected from standalone surveillance devices such as fixed surveillance cameras (CCTVs) or aerial devices (UAVs). Re-identifying vehicles across standalone surveillance systems is challenging when there is a severe illumination change, a change of viewpoint, or an occlusion. Cross platform surveillance (CCTV+UAV) based vehicle re-identification is yet to be explored and can mitigate some of the challenges faced during re-identifying vehicles with standalone surveillance systems. This paper proposes a novel cross platform vehicle identification dataset called MCU-VReID using 42 CCTVs and a UAV. A novel re-identification method called Multi-Scale Feature Fusion Transformer (MSFFT) is proposed to re-identify vehicles observed across the cross platform surveillance systems. The network consists of inception layers with transformer networks that enable it to learn the vehicle’s features at a variety of scales. The vehicles observed by two contrasting surveillance systems appear to be transformed representations of one another. Hence a two-stage training approach is facilitated for re-identifying vehicles observed across cross platform surveillance systems. The two-stage training approach aims to learn vehicle semantic transformations in the first stage using self-supervised approaches. The knowledge gained at the first stage relating to vehicle semantic transformations is transferred at the second stage of training to perform re-identification. Extensive experiments using the method demonstrate that MSFFT significantly improves over state-of-the-art methods to perform cross platform vehicle re-identification.

Multi-axis interactive multidimensional attention network for vehicle re-identification

Learning fine-grained discriminative information is essential to address the challenges of small inter-class differences and large intra-class differences in vehicle re-identification (Re-ID). Attentional mechanism is often used to capture important global information in images rather than fine-grained discriminative information. Studies have shown that the multi-axis interaction of information can enhance the feature representation ability of networks. This paper explores how to use the multi-axis interaction of information to facilitate more effective learning of attention and how to capture important detailed information in local regions. We propose a multi-axis interactive multidimensional attention network (MIMA-Net) for vehicle Re-ID. The network allows information to interact on multiple axes and calibrates the weight distribution of features from multiple dimensions to learn subtle discriminative information in vehicle parts/regions. The window-channel attention module (W-CAM) in MIMA-Net facilitates the learning of channel attention by interacting first across locations and then across channels, while the channel group-spatial attention module (CG-SAM) facilitates the learning of spatial attention by interacting first across channels and then across locations. These two modules perform window partitioning in a priori manner and channel semantic aggregation in an adaptive manner to learn discriminative semantic features in parts, respectively. These two approaches complement each other to strengthen the feature representation ability of MIMA-Net. Extensive experiments on three large public datasets, VeRi-776, VehicleID, and VERI-Wild, verify the effectiveness of our MIMA-Net and show that our method achieves state-of-the-art performance.

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.

Semantic-Oriented Feature Coupling Transformer for Vehicle Re-Identification in Intelligent Transportation System

More robust intelligent transportation systems including autonomous driving systems are in full flourish with the revolution of deep learning and the 6G wireless communication network. Vehicle Re-Identification, an indispensable branch of the intelligent transportation system, aims to retrieve specific vehicles captured from non-overlapping cameras. However, this is fundamentally challenging with the substantial inter-class similarity and substantial intra-class divergence. Embedding semantic information into vehicle re-identification task has gained ample interest, but the performance needs to be further improved. This work proposes a semantic-oriented feature coupling transformer (SOFCT) for vehicle re-identification as a solution. Specifically, the knowledge-based transformer is first embedded to model images with discriminative attributes. Second, original patches are divided into five semantic groups via semantics-patches coupling, and the feature extractions for different semantics are performed in the semantic feature extraction (SFE) transformer. Third, patch features are weighted via semantics-patches coupling in the patch feature weighting (PFW) transformer, the weighted feature is fed into subsequent encoders to excavate information. Finally, two groups of learnable semantics are embedded to automatically learn semantic features in the learnable semantic extraction (LSE) transformer. Experiments demonstrate that the proposed SOFCT method surpasses other state-of-the-arts with the mAP/Rank-1 of 80.7%/96.6%, 89.8%/84.5%, 86.4%/80.9%, and 84.3%/78.7% on VeRi776 and VehicleID.

Vehicle Re-Identification in Aerial Images and Videos: Dataset and Approach

Vehicle Re-Identification in Aerial Images and Videos: Dataset and Approach

PFNet: Part-guided feature-combination network for vehicle re-identification

PFNet: Part-guided feature-combination network for vehicle re-identification

Enhanced YOLOv5s + DeepSORT method for highway vehicle speed detection and multi-sensor verification

Addressing the need for vehicle speed measurement in traffic surveillance, this study introduces an enhanced scheme combining YOLOv5s detection with Deep SORT tracking. Tailored to the characteristics of highway traffic and vehicle features, the dataset data augmentation process was initially optimized. To improve the detector’s recognition capabilities, the Swin Transformer Block module was incorporated, enhancing the model’s ability to capture local regions of interest. CIoU loss was employed as the loss function for the vehicle detection network, accelerating model convergence and achieving higher regression accuracy. The Mish activation function was utilized to reduce computational overhead and enhance convergence speed. The structure of the Deep SORT appearance feature extraction network was modified, and it was retrained on a vehicle re-identification dataset to mitigate identity switches due to obstructions. Subsequently, using known references in the image such as lane markers and contour labels, the transformation from image pixel coordinates to actual coordinates was accomplished. Finally, vehicle speed was measured by computing the average of instantaneous speeds across multiple frames. Through radar and video Multi-Sensor Verification, the experimental results show that the mean Average Precision (mAP) for target detection consistently exceeds 90%. The effective measurement distance for speed measurement is around 140 m, with the absolute speed error generally within 1–8 km/h, meeting the accuracy requirements for speed measurement. The proposed model is reliable and fully applicable to highway scenarios.

UnbiasNet: Vehicle Re-Identification Oriented Unbiased Feature Enhancement by Using Causal Effect

UnbiasNet: Vehicle Re-Identification Oriented Unbiased Feature Enhancement by Using Causal Effect

Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification

Multi-Branch Enhanced Discriminative Network for Vehicle Re-Identification

A Blockchain-Enabled Distributed System for Trustworthy and Collaborative Intelligent Vehicle Re-Identification

A Blockchain-Enabled Distributed System for Trustworthy and Collaborative Intelligent Vehicle Re-Identification

A novel dual-pooling attention module for UAV vehicle re-identification

Vehicle re-identification (Re-ID) involves identifying the same vehicle captured by other cameras, given a vehicle image. It plays a crucial role in the development of safe cities and smart cities. With the rapid growth and implementation of unmanned aerial vehicles (UAVs) technology, vehicle Re-ID in UAV aerial photography scenes has garnered significant attention from researchers. However, due to the high altitude of UAVs, the shooting angle of vehicle images sometimes approximates vertical, resulting in fewer local features for Re-ID. Therefore, this paper proposes a novel dual-pooling attention (DpA) module, which achieves the extraction and enhancement of locally important information about vehicles from both channel and spatial dimensions by constructing two branches of channel-pooling attention (CpA) and spatial-pooling attention (SpA), and employing multiple pooling operations to enhance the attention to fine-grained information of vehicles. Specifically, the CpA module operates between the channels of the feature map and splices features by combining four pooling operations so that vehicle regions containing discriminative information are given greater attention. The SpA module uses the same pooling operations strategy to identify discriminative representations and merge vehicle features in image regions in a weighted manner. The feature information of both dimensions is finally fused and trained jointly using label smoothing cross-entropy loss and hard mining triplet loss, thus solving the problem of missing detail information due to the high height of UAV shots. The proposed method’s effectiveness is demonstrated through extensive experiments on the UAV-based vehicle datasets VeRi-UAV and VRU.

GLFNet: Combining Global and Local Information in Vehicle Re-Recognition.

Vehicle re-identification holds great significance for intelligent transportation and public safety. Extracting vehicle recognition information from multi-view vehicle images has become one of the challenging problems in the field of vehicle recognition. Most recent methods employ a single network extraction structure, either a single global or local measure. However, for vehicle images with high intra-class variance and low inter-class variance, exploring globally invariant features and discriminative local details is necessary. In this paper, we propose a Feature Fusion Network (GLFNet) that combines global and local information. It utilizes global features to enhance the differences between vehicles and employs local features to compactly represent vehicles of the same type. This enables the model to learn features with a large inter-class distance and small intra-class distance, significantly improving the model's generalization ability. Experiments show that the proposed method is competitive with other advanced algorithms on three mainstream road traffic surveillance vehicle re-identification benchmark datasets.

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.

Vehicle re-identification based on dimensional decoupling strategy and non-local relations.

Vehicle re-identification (Re-ID) is a challenging task that aims to recognize the same vehicle across different non-overlapping cameras. Existing attention mechanism-based methods for vehicle Re-ID often suffer from significant intra-class variation and inter-class variation due to various factors such as illumination, occlusion, viewpoint, etc. In this paper, we propose a novel network architecture for vehicle Re-ID, named Dimensional Decoupling Strategy and Non-local Relationship Network (DMNR-Net), which uses three modules to extract complementary features: global feature extraction module, non-local relationship capture module(NRCM), and dimensional decoupling module (DDS). The global feature extraction module captures complete and coarse-grained features from the whole image; the NRCM module extracts saliency information from feature maps in both spatial and channel dimensions; and the DDS decouples spatial and channel features into two branches to extract fine-grained features and focus on specific subspaces. We conduct extensive experiments on two popular publicly datasets, VeRi-776 and VehicleID, to evaluate the effectiveness of our method. The experimental results show that our DMNR-Net outperforms state-of-the-art methods by a large margin on both datasets.

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.

Vehicle Re-Identification Based on Convolution and Vision Transformer Feature Fusion

Vehicle Re-Identification Based on Convolution and Vision Transformer Feature Fusion

Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification

Multimodality Adaptive Transformer and Mutual Learning for Unsupervised Domain Adaptation Vehicle Re-Identification