Vehicle Re-Identification with Spatio-Temporal Model Leveraging by Pose View Embedding

Huang, Wenxin; Zhong, Xian; Jia, Xuemei; Liu, Wenxuan; Feng, Mingyue; Wang, Zheng; Satoh, Shin’ichi

doi:10.3390/electronics11091354

Cited by 4 publications

(2 citation statements)

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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%

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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Section: Vehicle Re-id Methodsmentioning

confidence: 99%

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

Wu,

Jin,

2023

Electronics

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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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