Trade-off background joint learning for unsupervised vehicle re-identification

Wang, Sheng; Wang, Qi; Min, Weidong; Han, Qing; Gai, Di; Luo, Haowen

doi:10.1007/s00371-023-03034-2

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…Studies have also shown that attributes, such as color, brand, and wheel pattern, can further improve re-ID efficacy [1,[20][21][22]. Other strategies [20,21,[23][24][25][26][27] have exploited the indirect attributes of a vehicle, such as camera perspective information and background information, making considerable improvements. These techniques, however, have overlooked valuable information present in the image beyond the vehicle itself, such as lighting conditions.…”

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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“…Combining detection and feature matching techniques, Ye et al [14] proposed a multi-target tracking algorithm based on the phenomenon that the target association accuracy of the multi-target tracking algorithm is reduced under frequent occlusions. All the proposed methods can effectively reduce the occlusion of the target, thus achieving stable tracking.…”

Section: Deep Learning Based Vehicle Counting Approachmentioning

confidence: 99%

Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification

Bi,

Yin,

Liu

et al. 2023

IJNDI

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Article Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification Yanchao Bi, Yuyan Yin, Xinfeng Liu *, Xiushan Nie, Chenxi Zou, and Junbiao Du 1 School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, China * Correspondence: liuxinfeng18@sdjzu.edu.cn Received: 17 June 2023 Accepted: 13 September 2023 Published: 26 September 2023 Abstract: Traffic detection devices can cause accuracy degradation over time. Considering problems such as time-consuming and laborious manual statistics, high misdetection probabilities, and model tracking failures, there is an urgent need to develop a deep learning model (which can stably achieve detection accuracy over 90%) to evaluate whether the device accuracy still satisfies the requirements or not. In this study, based on dual-granularity classification, a real-time traffic flow statistics method is proposed to address the above problems. The method is divided into two stages. The first stage uses YOLOv5 to acquire all the motorized and non-motorized vehicles appearing in the scene. The second stage uses EfficientNet to acquire the motorized vehicles obtained in the previous stage and classify such vehicles into six categories. Through this dual-granularity classification, the considered problem is simplified and the probability of false detection is reduced significantly. To correlate the front and back frames of the video, vehicle tracking is implemented using DeepSORT, and vehicle re-identification is implemented in conjunction with the ResNet50 model to improve the tracking accuracy. The experimental results show that the method used in this study solves the problems of misdetection and tracking effectively. Moreover, the proposed method achieves 98.7% real-time statistical accuracy by combining the two-lane counting method.

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Trade-off background joint learning for unsupervised vehicle re-identification

Cited by 4 publications

References 29 publications

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

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

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

Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification

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