Badminton-Analysis using Computer Vision

Introduction

This project utilizes computer vision techniques to detect players and shuttlecocks in badminton games. By leveraging advanced computer vision algorithms, aim to provide accurate detection of players and shuttlecocks to enhance the analysis of badminton matches.

Result

Result

Installation

1. Clone this repository:

   git clone https://github.com/ToanNguyenKhanh/Badminton-Analysis.git
   cd Badminton-Analysis
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Download pre-trained models:

   • Download the necessary pre-trained models for player and shuttlecock detection and place them in the models/ directory.

Usage

1. Prepare your dataset:

   • Ensure your dataset contains videos or images of badminton matches with annotated player and shuttlecock positions.
   • Organize the dataset into appropriate directories, such as 'train' and 'val' for training and validation data.

2. Train the model:

   • Modify the training configuration in the train.py file according to your dataset.

3. Analyze new videos or images:

   • Use the trained model to detect players and shuttlecocks in badminton matches

4. Result

Dataset Preparation

Ensure your dataset contains diverse videos or images of badminton matches, depicting various players, courts, and lighting conditions. Annotated bounding boxes should accurately enclose the players and shuttlecock in the frames.

Model Configuration

Experiment with different model configurations and adjust parameters such as input image size, batch size, and training epochs to optimize performance based on your specific requirements and computational resources.

Contributing

Contributions to this project are welcome. Feel free to submit pull requests for bug fixes, enhancements, or additional features.

License

This project is licensed under the MIT License. See the LICENSE file for details.