# A survey of advances in vision-based vehicle re-identification

arXiv: https://arxiv.org/abs/1905.13258 · HTML: https://mlsanity.com/paper/1905.13258/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

## Built on by (most cited first, 30 of 36)

- [Fine-Grained Image Analysis with Deep Learning: A Survey](https://mlsanity.com/paper/2111.06119/) (2021)
- [GeneCIS: A Benchmark for General Conditional Image Similarity](https://mlsanity.com/paper/2306.07969/) (2023)
- [Learning Compatible Embeddings](https://mlsanity.com/paper/2108.01958/) (2021)
- [Transformer for Object Re-Identification: A Survey](https://mlsanity.com/paper/2401.06960/) (2024)
- [Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography](https://mlsanity.com/paper/2103.15293/) (2021)
- [Adaptive Sparse Pairwise Loss for Object Re-Identification](https://mlsanity.com/paper/2303.18247/) (2023)
- [Applications of deep learning in traffic congestion detection, prediction and alleviation: A survey](https://mlsanity.com/paper/2102.09759/) (2021)
- [CoReS: Compatible Representations via Stationarity](https://mlsanity.com/paper/2111.07632/) (2021)
- [Survey on video anomaly detection in dynamic scenes with moving cameras](https://mlsanity.com/paper/2308.07050/) (2023)
- [Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data](https://mlsanity.com/paper/2211.11925/) (2022)
- [A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges](https://mlsanity.com/paper/2401.10643/) (2024)
- [Neighborhood Consensus Contrastive Learning for Backward-Compatible Representation](https://mlsanity.com/paper/2108.03372/) (2021)
- [AttributeNet: Attribute Enhanced Vehicle Re-Identification](https://mlsanity.com/paper/2102.03898/) (2021)
- [Towards All-in-One Medical Image Re-Identification](https://mlsanity.com/paper/2503.08173/) (2025)
- [Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing](https://mlsanity.com/paper/2304.06009/) (2023)
- [SBNet: Segmentation-based Network for Natural Language-based Vehicle Search](https://mlsanity.com/paper/2104.11589/) (2021)
- [VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network](https://mlsanity.com/paper/2312.06428/) (2023)
- [Leveraging Ensembles and Self-Supervised Learning for Fully-Unsupervised Person Re-Identification and Text Authorship Attribution](https://mlsanity.com/paper/2202.03126/) (2022)
- [MOTCOM: The Multi-Object Tracking Dataset Complexity Metric](https://mlsanity.com/paper/2207.10031/) (2022)
- [Style Variable and Irrelevant Learning for Generalizable Person Re-identification](https://mlsanity.com/paper/2209.05235/) (2022)
- [Machine Learning and Transformers for Thyroid Carcinoma Diagnosis: A Review](https://mlsanity.com/paper/2403.13843/) (2024)
- [Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification](https://mlsanity.com/paper/2304.07883/) (2023)
- [TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains](https://mlsanity.com/paper/2311.03124/) (2023)
- [Simple In-place Data Augmentation for Surveillance Object Detection](https://mlsanity.com/paper/2404.11226/) (2024)
- [Training-free Conditional Image Embedding Framework Leveraging Large Vision Language Models](https://mlsanity.com/paper/2512.21860/) (2025)
- [Parameter-Efficient Vision-Language Adaptation with Continuous Metadata Conditioning for Animal Re-Identification](https://mlsanity.com/paper/2607.09443/) (2026)
- [Pluggable Weakly-Supervised Cross-View Learning for Accurate Vehicle Re-Identification](https://mlsanity.com/paper/2103.05376/) (2021)
- [Multi-target tracking for video surveillance using deep affinity network: a brief review](https://mlsanity.com/paper/2110.15674/) (2021)
- [Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning](https://mlsanity.com/paper/2208.06579/) (2022)
- [Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data](https://mlsanity.com/paper/2305.00320/) (2023)

## Abstract (arXiv)

Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.