--> Football Analysis Project

Project Overview

The goal of this project is to detect and track players, referees, and footballs in a video using YOLO, a leading AI object detection model. I also trained the model to improve its performance. Additionally, we assigned players to teams based on the colors of their t-shirts using Kmeans for pixel segmentation and clustering. We also assigned the ball to a player during gameplay. With this information, we measured a team's ball acquisition percentage throughout the match. Furthermore, we implemented perspective transformation to convert the scene into a 2D graphic,, allowing us to measure a player's movement in meters rather than pixels.

Features

• Object Detection: Detect and track players, referees, and footballs using YOLO.
• Player-Team Assignment: Assign players to teams based on t-shirt colors using KMeans clustering.
• Ball Assignment: Assign the ball to a player during gameplay.
• Ball Possession Analysis: Measure team ball possession throughout the match.
• Movement Analysis: Track player movements and measure distances in meters using perspective transformation.

Tech Stack

• Programming Language: Python
• Framework: TensorFlow/PyTorch (for YOLO)
• Libraries: OpenCV, NumPy, scikit-learn, matplotlib, supervision, ultralytics
• Object Detection Model: YOLO (You Only Look Once)
• Clustering Algorithm: KMeans (for team assignment)
• Transformation: Perspective transformation (for real-world measurement)

Trained YOLO Model Results

Model Summary (Training)

Model Summary (Validation)

Result

Final Result (Demo)

output3.1.mp4

Challenges and Improvements

• Challenges:

  • Distinguishing players with similar t-shirt colors.
  • Tracking Players in complex game scenarios.

• Potential Improvements:

  • Training the YOLO with more Data (I only use a small DataSet)
  • Improve goalkepper team asigner
  • Use more advanced clustering techniques for better player-team assignment.