https://github.com/pjparties/footycv

https://github.com/pjparties/footycv

Last synced: 14 days ago
JSON representation

• Host: GitHub
• URL: https://github.com/pjparties/footycv
• Owner: pjparties
• Created: 2023-09-02T19:16:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-09-02T19:20:00.000Z (over 1 year ago)
• Last Synced: 2024-11-07T13:16:57.224Z (2 months ago)
• Size: 1.95 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# FootyCV

### Football Computer Vision Project

![Football Computer Vision](football_cv.png)

## Overview

This project leverages computer vision techniques to track players and balls in a football (soccer) match video footage. It aims to classify players into their respective teams and track the formation and attack/defense lines during a match.

## Table of Contents

- [Introduction](#introduction)

- [Features](#features)

- [How it Works](#how-it-works)

- [Contributing](#contributing)

- [License](#license)

## Introduction

Football (soccer) is a dynamic sport with constantly changing strategies and formations. Understanding team dynamics and the position of players and the ball on the field is crucial for tactical analysis and performance assessment. This computer vision project is designed to automate the process of tracking players and balls in football match footage, allowing for real-time or post-match analysis.

## Features

- Player tracking: Detect and track individual players throughout the match.

- Ball tracking: Follow the movement of the ball during the match.

- Team classification: Classify players into their respective teams (e.g., red team, blue team).

- Formation analysis: Determine the formation each team is playing (e.g., 4-4-2, 3-5-2).

- Attack and defense lines: Identify the positions of players forming attack and defense lines.

## How it Works

1. **Object Detection**: Utilizes object detection models to identify players and the ball in each frame of the video.

2. **Player Classification**: Classifies detected players into their respective teams based on jersey color or other distinguishing features.

3. **Player Tracking**: Tracks the movement of each player throughout the match.

4. **Ball Tracking**: Follows the ball's trajectory during the game.

5. **Formation Analysis**: Analyzes the positions of players on the field to determine the formation used by each team.

6. **Attack/Defense Line Detection**: Identifies the positions of players forming attack and defense lines.

## Contributing

Contributions to this project are welcome! If you have ideas for improvements, bug reports, or want to contribute new features, please check out the [contribution guidelines](CONTRIBUTING.md).

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

**Disclaimer**: This project is for educational and research purposes. It may require further development and customization for specific use cases and real-world scenarios.