|
--- |
|
license: mit |
|
language: |
|
- en |
|
tags: |
|
- dataset |
|
- AI |
|
- ML |
|
- object detection |
|
- hockey |
|
- puck |
|
metrics: |
|
- recall |
|
- precision |
|
- mAP |
|
datasets: |
|
- HockeyAI |
|
configs: |
|
- config_name: default |
|
data_files: |
|
- split: train |
|
path: data/train-* |
|
dataset_info: |
|
features: |
|
- name: image |
|
dtype: image |
|
splits: |
|
- name: train |
|
num_bytes: 409449992.92 |
|
num_examples: 1890 |
|
download_size: 363401335 |
|
dataset_size: 409449992.92 |
|
--- |
|
|
|
# HockeyAI: A Multi-Class Ice Hockey Dataset for Object Detection |
|
|
|
<div style="background-color:#f8f9fa; color:black; border-left: 6px solid #0073e6; padding: 10px; margin: 10px 0;"> |
|
|
|
🔗 This dataset is part of the <span style="color:red">HockeyAI</span> ecosystem. |
|
|
|
- 💻 Check out the corresponding <span style="color:red">Hugging Face Space</span> for a live demo: <a href="https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/spaces/SimulaMet-HOST/HockeyAI</a> |
|
- 🏒 The trained <span style="color:red">model</span> for this dataset is available here: <a href="https://huggingface.co/SimulaMet-HOST/HockeyAI" style="color:blue;">https://huggingface.co/SimulaMet-HOST/HockeyAI</a> |
|
|
|
</div> |
|
|
|
|
|
The **HockeyAI dataset** is an open-source dataset designed specifically for advancing computer vision research in ice hockey. With approximately **2,100 high-resolution frames** and detailed YOLO-format annotations, this dataset provides a rich foundation for tackling the challenges of object detection in fast-paced sports environments. |
|
|
|
The dataset is ideal for researchers, developers, and practitioners seeking to improve object detection and tracking tasks in ice hockey or similar dynamic scenarios. |
|
|
|
## Dataset Overview |
|
|
|
The HockeyAI dataset includes frames extracted from **broadcasted Swedish Hockey League (SHL) games**. Each frame is manually annotated, ensuring high-quality labels for both dynamic objects (e.g., players, puck) and static rink elements (e.g., goalposts, center ice). |
|
|
|
### Classes |
|
|
|
The dataset includes annotations for the following seven classes: |
|
- **centerIce**: Center circle on the rink |
|
- **faceoff**: Faceoff dots |
|
- **goal**: Goal frame |
|
- **goaltender**: Goalkeeper |
|
- **player**: Ice hockey players |
|
- **puck**: The small, fast-moving object central to gameplay |
|
- **referee**: Game officials |
|
|
|
 |
|
|
|
### Key Highlights: |
|
- **Resolution**: 1920×1080 pixels |
|
- **Frames**: ~2,100 |
|
- **Source**: Broadcasted SHL videos |
|
- **Annotations**: YOLO format, reviewed iteratively for accuracy |
|
- **Challenges Addressed**: |
|
- Motion blur caused by fast camera movements |
|
- Small object (puck) detection |
|
- Crowded scenes with occlusions |
|
|
|
## Applications |
|
|
|
The dataset supports a wide range of applications, including but not limited to: |
|
- **Player and Puck Tracking**: Enabling real-time tracking for tactical analysis. |
|
- **Event Detection**: Detecting goals, penalties, and faceoffs to automate highlight generation. |
|
- **Content Personalization**: Dynamically reframing videos to suit different screen sizes. |
|
- **Sports Analytics**: Improving strategy evaluation and fan engagement. |
|
|
|
## How to Use the Dataset |
|
|
|
1. Download the dataset from [Hugging Face](https://huggingface.co/your-dataset-link). |
|
|
|
2. The dataset is organized in the following structure: |
|
``` |
|
HockeyAI |
|
└── frames |
|
└── <Unique_ID>.jpg |
|
└── annotations |
|
└── <Unique_ID>.txt |
|
``` |
|
|
|
3. Each annotation file follows the YOLO format: |
|
``` |
|
<class_id> <x_center> <y_center> <width> <height> |
|
``` |
|
All coordinates are normalized to the image dimensions. |
|
|
|
4. Use the dataset with your favorite object detection framework, such as YOLOv8 or PyTorch-based solutions. |
|
|
|
|
|
|
|
<div style="background-color:#e7f3ff; color:black; border-left: 6px solid #0056b3; padding: 12px; margin: 10px 0;"> |
|
|
|
<span style="color:black; font-weight:bold;">📩 For any questions regarding this project, or to discuss potential collaboration and joint research opportunities, please contact:</span> |
|
|
|
<ul style="color:black;"> |
|
<li><span style="font-weight:bold; color:black;">Mehdi Houshmand</span>: <a href="mailto:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> |
|
<li><span style="font-weight:bold; color:black;">Cise Midoglu</span>: <a href="mailto:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> |
|
<li><span style="font-weight:bold; color:black;">Pål Halvorsen</span>: <a href="mailto:[email protected]" style="color:blue; text-decoration:none;">[email protected]</a></li> |
|
</ul> |
|
|
|
</div> |
|
|
|
|
|
|
