mlsanityData to 7 Oct 2026

A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges

arXiv 2401.10643 ↗First posted 19 Jan 2024cs.CV cs.AI cs.LGcited by 9

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Abstract (arXiv)

Vehicle re-identification (ReID) endeavors to associate vehicle images collected from a distributed network of cameras spanning diverse traffic environments. This task assumes paramount importance within the spectrum of vehicle-centric technologies, playing a pivotal role in deploying Intelligent Transportation Systems (ITS) and advancing smart city initiatives. Rapid advancements in deep learning have significantly propelled the evolution of vehicle ReID technologies in recent years. Consequently, undertaking a comprehensive survey of methodologies centered on deep learning for vehicle re-identification has become imperative and inescapable. This paper extensively explores deep learning techniques applied to vehicle ReID. It outlines the categorization of these methods, encompassing supervised and unsupervised approaches, delves into existing research within these categories, introduces datasets and evaluation criteria, and delineates forthcoming challenges and potential research directions. This comprehensive assessment examines the landscape of deep learning in vehicle ReID and establishes a foundation and starting point for future works. It aims to serve as a complete reference by highlighting challenges and emerging trends, fostering advancements and applications in vehicle ReID utilizing deep learning models.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 57 of its 137 references that are in the map. First posted on arXiv 19 Jan 2024.

Builds on

57 of 137 references.

References · 57

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38 more
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50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 3; 2026: 2.

9 papers.

  1. 2024Personalized Representation from Personalized Generationcited by 9
  2. 2025VLMs have Tunnel Vision: Evaluating Nonlocal Visual Reasoning in Leading VLMscited by 7
  3. 2024Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Surveycited by 4
  4. 2024Adaptive Aspect Ratios with Patch-Mixup-ViT-based Vehicle ReIDcited by 1
  5. 2024Towards Global Localization using Multi-Modal Object-Instance Re-Identificationcited by 0
  6. 2025CLIPVehicle: A Unified Framework for Vision-based Vehicle Searchcited by 0
  7. 2025Towards Visual Re-Identification of Fish using Fine-Grained Classification for Electronic Monitoring in Fisheriescited by 0
  8. 2026Zero-Shot Object Re-Identification in Egocentric Kitchen Videos via Multi-Stage SAM3 Feature Fusioncited by 0
  9. 2026Rethinking Multi-Branch and Cross-Backbone Fusion for Vehicle Re-Identification under Foundation-Model Pretrainingcited by 0