| --- |
| license: other |
| task_categories: |
| - keypoint-detection |
| - image-classification |
| tags: |
| - footwork |
| - keypoint-detection |
| - tactical-analysis |
| - sports |
| - computer-vision |
| - pose-estimation |
| pretty_name: Footwork Detection Keypoints Dataset |
| --- |
| |
| # Footwork Detection Keypoints Dataset |
|
|
| ## Dataset Description |
|
|
| This dataset was created from scratch for research and development in automated footwork detection and tactical analysis using computer vision and machine learning. |
|
|
| Unlike datasets collected from existing public benchmarks, this dataset was specifically constructed and organized by the authors for the footwork detection task. |
|
|
| ## Dataset Creation |
|
|
| The dataset was collected, processed, and annotated specifically for this project. The data contains keypoint-related information intended to support the detection and analysis of footwork patterns. |
|
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| All preprocessing and annotation procedures were performed as part of this project. |
|
|
| ## Intended Use |
|
|
| This dataset is intended for: |
|
|
| - Research in computer vision |
| - Footwork detection |
| - Keypoint-based analysis |
| - Sports analytics |
| - Tactical analysis |
| - Machine learning experimentation |
|
|
| ## Dataset Structure |
|
|
| The dataset is currently provided in Excel (`.xlsx`) format. |
|
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| The Excel files contain the keypoint and/or annotation information used during the development and evaluation of the proposed system. |
| ## Dataset Statistics |
|
|
| - Number of rows: 1,921 |
| - Dataset split: Train |
| - File format: CSV |
| - File size: approximately 1.36 MB |
| - Feature values: Numerical keypoint and motion-related features |
|
|
| ## Features |
|
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| The dataset contains numerical features related to footwork and movement analysis, including keypoint coordinates and derived movement characteristics. |
|
|
| Examples of features visible in the dataset include: |
|
|
| - `left_foot_y` |
| - `right_foot_y` |
| - `centroid_y_smooth` |
| - `trajectory_angle` |
| - `rolling_speed_mean` |
|
|
| Additional features are included in the dataset and are used by the proposed footwork detection and tactical analysis pipeline. |
|
|
| ## Custom Dataset Contribution |
|
|
| A primary contribution of this work is the creation of a custom dataset specifically designed for footwork detection and tactical analysis. |
|
|
| The dataset was not simply obtained from an existing benchmark; it was constructed from scratch for this research project. |
|
|
| ## Limitations |
|
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| The dataset may not represent every possible playing style, athlete, environment, camera angle, or tactical situation. Performance on unseen environments and populations may therefore differ from results obtained during development. |
|
|
| ## Citation |
|
|
| If you use this dataset in academic or research work, please cite the associated research paper: |
|
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| > Paper to be added after publication. |
|
|
| ## License |
|
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| Please check the licensing and redistribution requirements applicable to the underlying data before reuse. |
| ## Related Resources |
|
|
| ### Source Code |
|
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| The complete implementation, including YOLOv8n-based detection, MediaPipe keypoint extraction, Random Forest classification, and Streamlit application is available in the accompanying GitHub repository. |
| https://github.com/pranathi0690/badminton-database-dynamic - GITHUB LINK |
|
|
| ### Dataset |
|
|
| This dataset was created from scratch specifically for this project and contains the processed keypoint and movement-related features used during model development. |
|
|
| ### Research Paper |
|
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| The associated research paper will be added here upon publication. |
