# Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1911.05541 · HTML: https://mlsanity.com/paper/1911.05541/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2019.

## Built on by (most cited first, 14 of 14)

- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach](https://mlsanity.com/paper/2408.15103/) (2024)
- [Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark](https://mlsanity.com/paper/2408.11589/) (2024)
- [Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark](https://mlsanity.com/paper/2505.06393/) (2025)
- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [ICPR 2026 Competition on Low-Resolution License Plate Recognition](https://mlsanity.com/paper/2604.22506/) (2026)
- [Nighttime Driver Behavior Prediction Using Taillight Signal Recognition via CNN-SVM Classifier](https://mlsanity.com/paper/2310.16706/) (2023)
- [Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition](https://mlsanity.com/paper/2209.14448/) (2022)
- [Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution](https://mlsanity.com/paper/2408.15386/) (2024)
- [Efficient Video-Based ALPR System Using YOLO and Visual Rhythm](https://mlsanity.com/paper/2501.02270/) (2025)
- [Efficient License Plate Recognition in Videos Using Visual Rhythm and Accumulative Line Analysis](https://mlsanity.com/paper/2501.04750/) (2025)
- [CORE-ReID V2: Advancing the Domain Adaptation for Object Re-Identification with Optimized Training and Ensemble Fusion](https://mlsanity.com/paper/2508.04036/) (2025)
- [A Benchmark for Vehicle Attribute Classification in Cross-Domain Surveillance Scenarios](https://mlsanity.com/paper/2609.01584/) (2026)

## Abstract (arXiv)

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear.