version: 1.0.0
license: cc-by-nc-4.0
task_categories:
- object-detection
- video-classification
tags:
- sports
- soccer
- football
- ball-tracking
- tiny-object-detection
annotations_creators:
- human-verified
- machine-generated
pretty_name: Soccer Ball Tracking Dataset
size_categories:
- 1K<n<10K
Soccer Ball Tracking Dataset
A curated dataset for detecting and tracking soccer balls in broadcast footage, specifically designed for tiny object detection challenges. This dataset supports the development of models robust to motion blur, long-shot scales, and occlusions.
Dataset Description
This public sample consists of 1,450 frames extracted from 10 video clips of professional soccer broadcasts. The data is split into two categories based on ball visibility:
- Visible: Frames where the ball is clearly visible and annotated with a bounding box.
- Not Visible: Frames where the ball is occluded, out of frame, or otherwise not visible (negative samples).
This is a representative subset of a larger internal dataset, selected to cover diverse match conditions (SD/HD, different teams, mined vs. segmented clips).
Statistics
| Category | Samples | Description |
|---|---|---|
| Visible | 884 | Frames with at least one ball bounding box |
| Not Visible | 566 | Frames with no visible ball (hard negatives) |
| Total | 1,450 | Total frames from 10 clips |
Source Data
- Domain: Professional Soccer Broadcasts
- Resolution: Varied
- Annotation Style: YOLO format (normalized xywh)
- Labeling Method: Active Learning Loop (Model Mining -> Pseudo-labeling -> Manual Verification)
- Anonymization: Source video names have been replaced with UUIDs.
Dataset Structure
infactory-ai/ball-tracking/
├── README.md
├── metadata.csv
├── dataset_info.json
└── data/
├── visible/
│ ├── {uuid}_{frame}.jpg
│ └── {uuid}_{frame}.txt # YOLO label
└── not_visible/
└── {uuid}_{frame}.jpg
Metadata Fields (metadata.csv)
| Field | Type | Description |
|---|---|---|
file_path |
string | Relative path to the image file |
video_source |
string | UUID of the source video clip |
frame_index |
int | Frame number in the original clip |
visibility |
string | visible or not_visible |
bboxes_count |
int | Number of bounding boxes in the frame |
Usage
Loading with Hugging Face Datasets
from datasets import load_dataset
dataset = load_dataset("infactory-ai/ball-tracking", data_dir="data")
# Filter for visible frames
visible_frames = dataset.filter(lambda x: x["visibility"] == "visible")
Parsing Labels
Labels are in standard YOLO format:
<class_id> <x_center> <y_center> <width> <height>
class_id: 0 (ball)- Coordinates are normalized to [0, 1].
Team
| Name | Role |
|---|---|
| Valentino Constantinou | Head of Infrastructure |
| Dr. Mehdi Iranmanesh | Applied AI Engineer |
| John Kanalakis | Chief Technology Officer |
License
This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
You are free to:
- Share -- copy and redistribute the material in any medium or format
- Adapt -- remix, transform, and build upon the material
Under the following terms:
- Attribution -- You must give appropriate credit to Infactory, provide a link to the license, and indicate if changes were made.
- Non-Commercial -- You may not use the material for commercial purposes without a separate commercial license from Infactory.
Commercial licensing: For commercial use, contact hello@infactory.ai.
Citation
@dataset{ball_tracking_2026,
title={Soccer Ball Tracking Dataset},
author={Constantinou, Valentino and Iranmanesh, Mehdi and Kanalakis, John},
year={2026},
publisher={Infactory},
url={https://huggingface.co/datasets/infactory-ai/ball-tracking}
}