Attributes Guided Feature Learning for Vehicle Re-Identification

Zheng, Aihua; Lin, Xianmin; Li, Chenglong; He, Ran; Tang, Jin

doi:10.1109/tetci.2021.3127906

Cited by 23 publications

(8 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Jiang et al [10] presented a multi-branch architecture that extracted color, model, and appearance features to comprehensively characterize a vehicle. Similarly, Li et al [11] introduced the DF-CVTC (Deep Feature with Camera Views, Vehicle Types, and Colors) for vehicle re-identification. To incorporate viewpoint information, Li et al [12] introduced a method named Viewpoint-Aware Re-Identification (VARID), which employed viewpoint clustering and deep metric learning to acquire discriminative features.…”

Section: Visual Feature-based Vehicle Re-identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…With the significant progress of deep learning in computer vision, many methods [10][11][12][13][14][15][16][17][18] now utilize neural networks to adaptively extract high-level features from vehicle images, making them a primary and efficient approach for vehicle re-identification. Some methods [10][11][12][13] focus on embedding a vehicle image into a global feature using CNNs. Nevertheless, they underperform when different vehicles share significantly similar appearance attributes (e.g., Figure 1a) or when the same vehicle exhibits diverse appearances (e.g., Figure 1b), namely, the inter-class similarity and the intra-class difference issues.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

An,

Zhang,

Shi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Visual Feature-based Vehicle Re-identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

An,

Zhang,

Shi

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Vehicle Re-identification (ReID) is an important and challenging task in the computer vision literature for visual surveillance applications. A large number of models for ReID problem have been proposed [1,16,20,26,41], exploring various architectures, deep metric learning methods and image enhancement techniques. The vast majority of ReID approaches have been based on visible spectrum visual data, as traditional RGB sensors have been the most Figure 1.…”

Section: Introductionmentioning

confidence: 99%

A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification

Kamenou,

Del Rincón,

Miller

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

View full text Add to dashboard Cite

Recent advances in imaging technologies have enabled the usage of infrared spectrum data for computer vision tasks previously working with traditional RGB data, such as re-identification. Infrared spectrum data can provide complementary and consistent visual information in situations of low visibility such as night-time, or adverse environments. However, the main issue that prevents the training of multi-modal systems is the lack of available infrared spectrum data. To this end, it is important to create systems that can easily adapt to data of multiple modalities, at inference time. In this paper, we propose a domain generalisation approach for multi-modal vehicle re-identification based on the recent success of meta-learning training approaches, and evaluate the ability of the model to perform to unseen modality data at testing time. In our experiments we use RGB, near-infrared and thermal-infrared modalities using the RGBNT100 dataset and prove that our meta-learning training configuration can improve the generalisation ability of the trained model compared to traditional training settings.

show abstract

“…Thanks to the success of deep learning, the vehicle identification algorithm in the field of surveillance cameras again achieved impressive results [5][6][7][8][9]. According to the idea of solving the vehicle ReID problem, the methods of vehicle ReID can be divided into a global feature-based method, local feature-based method, attention mechanism-based method, vehicle perspective-based method, and generative adversarial network-based method.…”

Section: Introductionmentioning

confidence: 99%

A Novely Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Yin,

Peng,

et al. 2023

Preprint

View full text Add to dashboard Cite

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.

show abstract

Attributes Guided Feature Learning for Vehicle Re-Identification

Cited by 23 publications

References 54 publications

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

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

A Meta-learning Approach for Domain Generalisation across Visual Modalities in Vehicle Re-identification

A Novely Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification

Contact Info

Product

Resources

About