mlsanityData to 7 Oct 2026

Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review

arXiv 2102.09744 ↗First posted 19 Feb 2021cs.CV cs.AI cs.MMcited by 6

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Abstract (arXiv)

Vehicle Re-identification (re-id) over surveillance camera network with non-overlapping field of view is an exciting and challenging task in intelligent transportation systems (ITS). Due to its versatile applicability in metropolitan cities, it gained significant attention. Vehicle re-id matches targeted vehicle over non-overlapping views in multiple camera network. However, it becomes more difficult due to inter-class similarity, intra-class variability, viewpoint changes, and spatio-temporal uncertainty. In order to draw a detailed picture of vehicle re-id research, this paper gives a comprehensive description of the various vehicle re-id technologies, applicability, datasets, and a brief comparison of different methodologies. Our paper specifically focuses on vision-based vehicle re-id approaches, including vehicle appearance, license plate, and spatio-temporal characteristics. In addition, we explore the main challenges as well as a variety of applications in different domains. Lastly, a detailed comparison of current state-of-the-art methods performances over VeRi-776 and VehicleID datasets is summarized with future directions. We aim to facilitate future research by reviewing the work being done on vehicle re-id till to date.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 39 of its 149 references that are in the map. First posted on arXiv 19 Feb 2021.

Builds on

39 of 149 references.

References · 39

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2014Going Deeper with Convolutionscited by 4,231
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  6. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,421
  7. 2017Dynamic Routing Between Capsulescited by 689
  8. 2015An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognitioncited by 519
  9. 2017Person Transfer GAN to Bridge Domain Gap for Person Re-Identificationcited by 479
  10. 2017Residual Attention Network for Image Classificationcited by 385
  11. 2017Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitrocited by 280
  12. 2015Bilinear CNNs for Fine-grained Visual Recognitioncited by 228
27 more
  1. 2017Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identificationcited by 168
  2. 2014Deep Learning Face Representation by Joint Identification-Verificationcited by 162
  3. 2015A Large-Scale Car Dataset for Fine-Grained Categorization and Verificationcited by 129
  4. 2019CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identificationcited by 119
  5. 2017Disentangled Person Image Generationcited by 98
  6. 2017Deeply-Learned Part-Aligned Representations for Person Re-Identificationcited by 68
  7. 2015Action Recognition using Visual Attentioncited by 49
  8. 2017Pose Invariant Embedding for Deep Person Re-identificationcited by 47
  9. 2019A Dual-Path Model With Adaptive Attention For Vehicle Re-Identificationcited by 34
  10. 2018RAM: A Region-Aware Deep Model for Vehicle Re-Identificationcited by 30
  11. 2015Fine-grained Categorization and Dataset Bootstrapping using Deep Metric Learning with Humans in the Loopcited by 25
  12. 2016Learning a Discriminative Null Space for Person Re-identificationcited by 24
  13. 2017BoxCars: Improving Fine-Grained Recognition of Vehicles using 3-D Bounding Boxes in Traffic Surveillancecited by 23
  14. 2017Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposalscited by 23
  15. 2019Stripe-based and Attribute-aware Network: A Two-Branch Deep Model for Vehicle Re-identificationcited by 22
  16. 2019Vehicle Re-Identification: an Efficient Baseline Using Triplet Embeddingcited by 21
  17. 2018Vehicle Re-identification Using Quadruple Directional Deep Learning Featurescited by 14
  18. 2020StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identificationcited by 14
  19. 2019Attributes Guided Feature Learning for Vehicle Re-identificationcited by 12
  20. 2016Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMscited by 9
  21. 2019Part-Guided Attention Learning for Vehicle Instance Retrievalcited by 9
  22. 2018Vehicle Re-Identification in Contextcited by 7
  23. 2018Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identificationcited by 6
  24. 2019Variational Representation Learning for Vehicle Re-Identificationcited by 4
  25. 2014Deep Metric Learning for Practical Person Re-Identificationcited by 3
  26. 2015Part Localization using Multi-Proposal Consensus for Fine-Grained Categorizationcited by 3
  27. 2019A Two-Stream Siamese Neural Network for Vehicle Re-Identification by Using Non-Overlapping Camerascited by 1

Built on by

Cited by year

Citing papers per year: 2021: 3; 2022: 0; 2023: 0; 2024: 3.

6 papers.

  1. 2024A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challengescited by 9
  2. 2021UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagerycited by 3
  3. 2021SBNet: Segmentation-based Network for Natural Language-based Vehicle Searchcited by 3
  4. 2024Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle ReIDcited by 1
  5. 2021Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scanscited by 0
  6. 2024Study on Aspect Ratio Variability toward Robustness of Vision Transformer-based Vehicle Re-identificationcited by 0