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Pranathi196/footwork-detection-keypoints

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… See the full description on the dataset page: https://huggingface.co/datasets/Pranathi196/footwork-detection-keypoints.

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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.

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.

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

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

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:

Paper to be added after publication.

License

Please check the licensing and redistribution requirements applicable to the underlying data before reuse.

Related Resources

Source Code

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

The associated research paper will be added here upon publication.