Datasets:
language:
- en
license: mit
task_categories:
- object-detection
pretty_name: ExposureEngine
tags:
- Oriented Logo Detection
- Oriented Object Detection
- Sponsor Visibility
- logo
- detection
ExposureEngine: Oriented Logo Detection & Sponsor Visibility Analytics (Dataset)
Rotation-aware OBB annotations for sponsor logos in professional soccer broadcasts — built for sports analytics, YOLO OBB training, and sponsorship measurement.
ExposureEngine provides high-quality oriented bounding box (OBB) polygon annotations of sponsor logos from professional soccer footage to enable accurate sponsor visibility measurement, logo tracking, and rotation-aware detection.
📂 Dataset Overview
- Source: 97 broadcast highlight clips from the 2024 Swedish Elite Soccer League
- Frames: 1,103 curated frames (sampled at 1 FPS)
- Annotations: 670 unique sponsor logos with OBB polygon coordinates (YOLO format)
- Coverage: 32 matches · 16 teams · diverse events: Goals, Shots, Yellow Cards, Offsides, Substitutions
- Splits: Train / Validation / Test = 80 / 10 / 10
🔍 Why Oriented Bounding Boxes?
OBB captures rotation, skew, and perspective — common in sports due to dynamic camera angles, jersey folds, and slanted boards. This improves coverage accuracy and downstream analytics over traditional HBB.
🚀 Applications
- Sports sponsorship analytics & reporting
- YOLO-based OBB training & benchmarking
- Brand visibility measurement in broadcasts
- Content adaptation for social media & OTT
⚡ Quick Start (🤗 Datasets)
from datasets import load_dataset
dataset = load_dataset("SimulaMet-HOST/ExposureEngine") print(dataset)
Each label file contains four-corner polygon coordinates per logo in YOLO OBB format.
📄 Citation
If you use this dataset, please cite the paper:
ExposureEngine: Oriented Logo Detection and Sponsor Visibility Analytics in Sports Broadcasts.
📬 Contact
Email: mehdi@forzasys.com