VRAI Vehicle Re-identification Dataset
The VRAI dataset supports vehicle re‑identification research in aerial imagery. With the rapid growth of unmanned aerial vehicles (UAVs), UAV‑based visual applications have attracted increasing attention from both industry and academia. However, public UAV vehicle re‑ID datasets are scarce, limiting research despite potential applications such as long‑term tracking and visual object retrieval. VRAI addresses this gap by providing a large‑scale, publicly available collection of UAV‑captured vehicle images with comprehensive annotations.
Dataset description and usage context
Dataset Overview
Name: VRAI Vehicle Re‑identification Dataset
Purpose: Supports vehicle re‑identification research using aerial imagery.
Background: The rapid growth of UAVs has spurred interest in UAV‑based visual applications. Yet, vehicle re‑ID research using UAV data remains limited due to the lack of publicly available datasets, which require extensive UAV flights, video capture, and manual annotation.
Dataset Splits
- Training set: 66,113 images, 6,302 IDs.
- Test set: 71,500 images, 6,720 IDs.
- Test‑dev set: 20 % of test images sampled for development.
Annotation Details
Training Set
- Image naming:
ID_Cam_Frame.jpg(e.g.,0000000X_000Y_0000000Z.jpg). - Annotation file:
train_annotation.pklcontaining image filenames, color labels, type labels, vehicle attribute labels, and bounding boxes for vehicle parts.
Test Set
- Image naming:
RandomString_Cams.jpg(e.g.,00AV11D2_C1.jpg). - Annotation file:
test_annotation.pklwith similar fields plus gallery and query ordering.
Test‑dev Set
- Same naming convention and annotation structure as the test set.
Evaluation Metrics
- Metrics: Mean Average Precision (mAP) and Cumulative Matching Curve (CMC).
- Challenge platform: EvalAI.
Dataset Download
Usage Statements
- Commercial use: Prohibited.
- Citation requirement: Must cite relevant publications when using the dataset.
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