DFR-ST: Discriminative feature representation with spatio-temporal cues for vehicle re-identification

Tu, Jingzheng; Chen, Cailian; Huang, Xiaolin; He, Jianxing; Guan, Xinping

doi:10.1016/j.patcog.2022.108887

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…We compared our method with some state-of-the-art (SOTA) approaches from the last three years, categorized into three groups: (1) Global feature-based (GF) methods, such as SN [13], VARID [12], VAT [18], and MsKAT [32], mainly concentrate on extracting whole representation for vehicle images. (2) Local feature-based (LF) methods, including DPGM [14], LG-CoT [36], HPGN [37], DFR [38], DSN [39], SFMNet [40], GiT [31], SOFCT [22], MART [41] integrate local features with the global feature to learn reliable vehicle representations. (3) Spatial-temporal (ST) methods, such as DPGM-ST [14] and DFR-ST [38], exploit extra timestamp and camera location information to enhance vehicle re-identification using visual features.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

“…(2) Local feature-based (LF) methods, including DPGM [14], LG-CoT [36], HPGN [37], DFR [38], DSN [39], SFMNet [40], GiT [31], SOFCT [22], MART [41] integrate local features with the global feature to learn reliable vehicle representations. (3) Spatial-temporal (ST) methods, such as DPGM-ST [14] and DFR-ST [38], exploit extra timestamp and camera location information to enhance vehicle re-identification using visual features. The Baseline refers to our model using only a branch for global feature learning without the SFM.…”

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

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

An,

Zhang,

Shi

et al. 2024

Applied Sciences

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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.

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Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

Section: Comparisons With State-of-the-art Methodsmentioning

confidence: 99%

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

An,

Zhang,

Shi

et al. 2024

Applied Sciences

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“…Liu et al [12] proposed "PROVID" and made progress with the use of license plate information. Other studies [13,14] have shown that spatial and temporal information from vehicle images have contributed to improving vehicle re-ID performance. For example, PROVID [12] re-ranks vehicles using spatio-temporal properties based on a simple from-near-distant principle.…”

Section: Vehicle Re-id Methodsmentioning

confidence: 99%

“…GSTN [40] automatically located vehicles and performed division for regional features to produce robust part-based features for re-ID. DFR-ST [13] involved appearance and spatio-temporal information to build robust features in the embedding space. DSN [41] utilized a cross-region attention to enhance spatial awareness of local features.…”

Section: Comparison With Sota Methodsmentioning

confidence: 99%

Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions

Wu,

Jin,

2023

Electronics

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Vehicle matching in variable illumination environments can be challenging due to the heavy dependence of vehicle appearance on lighting conditions. To address this issue, we propose a two-stage progressive learning (TSPL) framework. In the first stage, illumination-aware metric learning is enforced using a two-branch network via two illumination-specific feature spaces, used to explicitly model differences in lighting. In the second stage, discriminative feature learning is introduced to extract distinguishing features from a given vehicle. This process consists of a local feature extraction attention module, a local constraint, and a balanced sampling strategy. During the metric learning phase, the model expresses the union of local features, extracted from the attention module, with illumination-specific global features to form joint vehicle features. As part of the study, we construct a large-scale dataset, termed VERI-DAN (vehicle re-identification across day and night), to address the current lack of vehicle datasets exhibiting variable lighting conditions. This set is composed of 200,004 images from 16,654 vehicles, collected in various natural illumination environments. Validation experiments conducted with the VERI-DAN and Vehicle-1M datasets demonstrated that our proposed methodology effectively improved vehicle re-identification Rank-1 accuracy.

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“…Method Type Advantage Limitation [19,20] Deep learning High recognition accuracy High cost; poor interpretability [21,22] Spatiotemporal information Works well for hard samples Additional complex spatiotemporal labels are required [16,17] Metrics learning High recognition accuracy High cost [23,24] Multidimensional information based…”

Section: Referencementioning

confidence: 99%

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

Yang,

Liu,

Huang

et al. 2024

Sensors

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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.

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DFR-ST: Discriminative feature representation with spatio-temporal cues for vehicle re-identification

Cited by 9 publications

References 22 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

Two-Stage Progressive Learning for Vehicle Re-Identification in Variable Illumination Conditions

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

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