# Vehicle Re-Identification: an Efficient Baseline Using Triplet Embedding

arXiv: https://arxiv.org/abs/1901.01015 · HTML: https://mlsanity.com/paper/1901.01015/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2019.

## Built on by (most cited first, 21 of 21)

- [GiT: Graph Interactive Transformer for Vehicle Re-identification](https://mlsanity.com/paper/2107.05475/) (2021)
- [TripletTrack: 3D Object Tracking using Triplet Embeddings and LSTM](https://mlsanity.com/paper/2210.16204/) (2022)
- [Heterogeneous Relational Complement for Vehicle Re-identification](https://mlsanity.com/paper/2109.07894/) (2021)
- [View-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network](https://mlsanity.com/paper/2403.14513/) (2024)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [A Comprehensive Survey on Deep-Learning-based Vehicle Re-Identification: Models, Data Sets and Challenges](https://mlsanity.com/paper/2401.10643/) (2024)
- [LATex: Leveraging Attribute-based Text Knowledge for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2503.23722/) (2025)
- [SD-ReID: View-aware Stable Diffusion for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2504.09549/) (2025)
- [Trends in Vehicle Re-identification Past, Present, and Future: A Comprehensive Review](https://mlsanity.com/paper/2102.09744/) (2021)
- [HSGNet: Object Re-identification with Hierarchical Similarity Graph Network](https://mlsanity.com/paper/2211.05486/) (2022)
- [GSAlign: Geometric and Semantic Alignment Network for Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2510.22268/) (2025)
- [UAV-ReID: A Benchmark on Unmanned Aerial Vehicle Re-identification in Video Imagery](https://mlsanity.com/paper/2104.06219/) (2021)
- [Negative Samples are at Large: Leveraging Hard-distance Elastic Loss for Re-identification](https://mlsanity.com/paper/2207.09884/) (2022)
- [UAV (Unmanned Aerial Vehicles): Diverse Applications of UAV Datasets in Segmentation, Classification, Detection, and Tracking](https://mlsanity.com/paper/2409.03245/) (2024)
- [Instance and Pair-Aware Dynamic Networks for Re-Identification](https://mlsanity.com/paper/2103.05395/) (2021)
- [Global-Supervised Contrastive Loss and View-Aware-Based Post-Processing for Vehicle Re-Identification](https://mlsanity.com/paper/2204.07943/) (2022)
- [Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning](https://mlsanity.com/paper/2208.06579/) (2022)
- [Complete Solution for Vehicle Re-ID in Surround-view Camera System](https://mlsanity.com/paper/2212.04126/) (2022)
- [Self-supervised Geometric Features Discovery via Interpretable Attentio for Vehicle Re-Identification and Beyond (Complete Version)](https://mlsanity.com/paper/2303.11169/) (2023)
- [ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification](https://mlsanity.com/paper/2311.02734/) (2023)
- [Rectifying Geometry-Induced Similarity Distortions for Real-World Aerial-Ground Person Re-Identification](https://mlsanity.com/paper/2601.21405/) (2026)

## Abstract (arXiv)

In this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle re-identification literature. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.