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Football Tracking using YOLOv8 and OpenCV

This repository contains a football analysis system that leverages machine learning, computer vision, and deep learning techniques to analyze football matches. The system utilizes state-of-the-art object detection models, custom object detectors, pixel segmentation, optical flow, perspective transformation, and speed-distance measurement to provide comprehensive insights into player movements and match dynamics.

Features

  1. Object Detection with YOLOv8: Utilizes Ultralytics and YOLOv8 to detect players, referees, and footballs in images and videos.

  2. Custom Object Detection: Fine-tunes and trains a custom YOLO model on a dataset tailored specifically for football analysis, enhancing object detection accuracy.

  3. Pixel Segmentation with KMeans: Employs KMeans clustering to segment players from the background based on the colors of their t-shirts, providing accurate player identification.

  4. Optical Flow for Camera Movement: Implements optical flow techniques to measure camera movement between frames, ensuring precise analysis of player movements.

  5. Perspective Transformation: Utilizes OpenCV's perspective transformation to represent the scene's depth and perspective, enabling measurements of player movement in meters rather than pixels.

  6. Speed and Distance Measurement: Calculates players' speed and distance covered in the image, offering valuable insights into player performance.

Getting Started

To get started with using the football analysis system, follow these steps:

  1. Clone this repository to your local machine:

    git clone https://github.com/AnshChoudhary/Football-Tracking.git

  2. Install the required dependencies:

    pip install -r requirements.txt

  3. Run the provided scripts or integrate the system into your own project as needed.

Usage

The system provides various scripts and modules for different tasks:

  • yolo_inference.py: Perform object detection using YOLOv8.
  • football_training_yolo_v5.ipynb: Fine-tune and train a custom YOLO model.
  • team_assigner.py: Segment players from the background using KMeans.
  • camera_movement_estimator.py: Measure camera movement using optical flow.
  • view_transformer.py: Apply perspective transformation to represent scene depth.
  • speed_and_distance_estimator.py: Calculate player speed and distance covered.

You can use these scripts individually or combine them to achieve specific analysis tasks.

Contribution

Contributions to this project are welcome! If you have any ideas for improvements or new features, feel free to open an issue or submit a pull request.

License

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

Acknowledgments

  • Ultralytics for providing the YOLOv8 implementation.
  • OpenCV for the powerful computer vision functionalities.
  • Contributors to various libraries and frameworks used in this project.