Viewpoint Alignment and Discriminative Parts Enhancement in 3D Space for Vehicle ReID

Meng, Dechao; Zhang, Lipeng; Liu, Xuejing; Gao, Lin; Huang, Qingming

doi:10.1109/tmm.2022.3154102

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…In this section, we are going to describe our proposed solution (including future evolutions). As stated in the introduction, most of the above-mentioned proposals for vehicle identification act in isolation, or they combine just one AI technology with different physical sensors (as in [64]). Ours tries to show a new approach by offering both license plate recognition and vehicle re-identification; at the same time, combining everything in a multinet solution.…”

Section: Proposed Method: Double-factor Authentication For Vehicle Id...mentioning

confidence: 99%

“…Metrics mAP r@ank1 r@ank5 GRMF [61] Multi-granularity feature learning 0.882 0.957 0.991 VARID [62] Inter-and intra-view triplet loss 0.793 0.962 0.992 SN++ [63] Support neighbours loss 0.757 0.951 0.981 Meng et al [64] 3D viewpoint alignment 0.832 0.987 0.992…”

Section: Model Characteristicsmentioning

confidence: 99%

“…The second one proposes the "support neighbours (SN) loss" derived from the KNN (k nearest neighbours) algorithm, and it is also valid for person and vehicle re-identification. The study in [64] is another very interesting and novel approach, due to its combination of "3D viewpoint alignment".…”

Section: Model Characteristicsmentioning

confidence: 99%

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Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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Security cameras have been proven to be particularly useful in preventing and combating crime through identification tasks. Here, two areas can be mainly distinguished: person and vehicle identification. Automatic license plate readers are the most widely used tool for vehicle identification. Although these systems are very effective, they are not reliable enough in certain circumstances. For example, due to traffic jams, vehicle position or weather conditions, the sensors cannot capture an image of the entire license plate. However, there is still a lot of additional information in the image which may also be of interest, and that needs to be analysed quickly and accurately. The correct use of the processing mechanisms can significantly reduce analysis time, increasing the efficiency of video cameras significantly. To solve this problem, we have designed a solution based on two technologies: license plate recognition and vehicle re-identification. For its development and testing, we have also created several datasets recreating a real environment. In addition, during this article, it is also possible to read about some of the main artificial intelligence techniques for these technologies, as they have served as the starting point for this research.

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Section: Proposed Method: Double-factor Authentication For Vehicle Id...mentioning

confidence: 99%

Section: Model Characteristicsmentioning

confidence: 99%

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Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

2022

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“…Previous vehicle Re-ID methods [19][20][21] relied on manually labeled vehicle viewpoints to produce robust cross-view representations. Wang et al [22] introduced a novel vehicle Re-ID framework, including an orientation-invariant feature-embedding module and a spatialtemporal regularization module The former module can better extract and align local region features, and the latter can make the retrieval results more refined.…”

Section: Multi-view Learning For Vehicle Re-idmentioning

confidence: 99%

Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification

Zhou,

Wang,

Cao

et al. 2024

Electronics

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The definition of vehicle viewpoint annotations is ambiguous due to human subjective judgment, which makes the cross-domain vehicle re-identification methods unable to learn the viewpoint invariance features during source domain pre-training. This will further lead to cross-view misalignment in downstream target domain tasks. To solve the above challenges, this paper presents a dual-level viewpoint-learning framework that contains an angle invariance pre-training method and a meta-orientation adaptation learning strategy. The dual-level viewpoint-annotation proposal is first designed to concretely redefine the vehicle viewpoint from two aspects (i.e., angle-level and orientation-level). An angle invariance pre-training method is then proposed to preserve identity similarity and difference across the cross-view; this consists of a part-level pyramidal network and an angle bias metric loss. Under the supervision of angle bias metric loss, the part-level pyramidal network, as the backbone, learns the subtle differences of vehicles from different angle-level viewpoints. Finally, a meta-orientation adaptation learning strategy is designed to extend the generalization ability of the re-identification model to the unseen orientation-level viewpoints. Simultaneously, the proposed meta-learning strategy enforces meta-orientation training and meta-orientation testing according to the orientation-level viewpoints in the target domain. Extensive experiments on public vehicle re-identification datasets demonstrate that the proposed method combines the redefined dual-level viewpoint-information and significantly outperforms other state-of-the-art methods in alleviating viewpoint variations.

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“…V EHICLE Re-identification (Re-ID) is an important task in intelligent transportation systems [1,2,3,4,5], as it allows for the retrieval of the same vehicle from multiple non-overlapping surveillance cameras. With the availability of vehicle surveillance datasets [6,7,8,9], many vehicle Re-ID models have been proposed [10,11,12,13], which have made significant progress in the past decade and gained wide interest among the research communities of human-machine systems, cybernetics, and transportation.…”

Section: Introductionmentioning

confidence: 99%

Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification

Zheng

et al. 2022

IEEE Trans. on Image Process.

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Vehicle Re-identification (Re-ID) has been broadly studied in the last decade; however, the different camera view angle leading to confused discrimination in the feature subspace for the vehicles of various poses, is still challenging for the Vehicle Re-ID models in the real world. To promote the Vehicle Re-ID models, this paper proposes to synthesize a large number of vehicle images in the target pose, whose idea is to project the vehicles of diverse poses into the unified target pose so as to enhance feature discrimination. Considering that the paired data of the same vehicles in different traffic surveillance cameras might be not available in the real world, we propose the first Pair-flexible Pose Guided Image Synthesis method for Vehicle Re-ID, named as VehicleGAN in this paper, which works for both supervised and unsupervised settings without the knowledge of geometric 3D models. Because of the feature distribution difference between real and synthetic data, simply training a traditional metric learning based Re-ID model with data-level fusion (i.e., data augmentation) is not satisfactory, therefore we propose a new Joint Metric Learning (JML) via effective feature-level fusion from both real and synthetic data. Intensive experimental results on the public VeRi-776 and VehicleID datasets prove the accuracy and effectiveness of our proposed VehicleGAN and JML.

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Viewpoint Alignment and Discriminative Parts Enhancement in 3D Space for Vehicle ReID

Cited by 6 publications

References 29 publications

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

Intelligent Video Surveillance Systems for Vehicle Identification Based on Multinet Architecture

Dual-Level Viewpoint-Learning for Cross-Domain Vehicle Re-Identification

Attribute and State Guided Structural Embedding Network for Vehicle Re-Identification

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