Football Analysis Project

Overview

• The project involves detecting and tracking players, referees, and footballs in a video using YOLO (You Only Look Once), an advanced AI object detection model.
• Key objectives:
  • Train YOLO to enhance detection accuracy.
  • Segment and cluster pixels based on T-shirt colors for team assignments.
  • Measure ball acquisition percentage.
  • Utilize optical flow to analyze camera movement and player movement.
  • Implement perspective transformation to accurately represent scene depth.
  • Measure player speed and distance covered.

Getting Started

• Dataset: Use the DFL Bundesliga dataset for football matches, accessible via Gaggle.
• Environment Setup: Create a folder named football_analysis and an input_videos subfolder for video files.

Object Detection with YOLO

• Install the Ultralytics library for YOLO.
• Create a file named yolo_inference.py for experimenting with YOLO.
• Load YOLO model (preferably YOLOv8x for accuracy).
• Perform object detection on the video and print bounding box results.

Training YOLO for Improved Accuracy

• Train YOLO using a specific dataset for better detection of players and the ball.
• Use Robo Flow to download the required dataset and annotations for training.
• Train the model using Google Colab to leverage GPU resources.

Object Tracking

• Understand the concept of tracking in object detection to maintain identities across frames.
• Implement a tracker (ByteTracker) that can track players and the ball across video frames.
• Create a tracker.py file containing the tracking logic, including tracking and ID assignment.

Color Segmentation for Team Assignment

• Utilize K-means clustering to assign players to teams based on T-shirt colors.
• Create a Notebook for color assignment analysis and visualize results.
• Implement functions in a new team_assigner.py file to handle team color assignments.

Measuring Camera Movement

• Analyze camera movement using optical flow between frames to adjust player movement calculations.
• Use cv2.goodFeaturesToTrack to analyze corner features in the video.
• Create a new camera_movement.py file for calculating and handling camera movement.

Perspective Transformation

• Implement a view_transformer.py to map pixel coordinates to real-world measurements.
• Use perspective transformation to adjust player movement metrics based on the camera's distorted viewpoint.

Speed and Distance Calculation

• Create a speed_distance_estimator.py file to calculate player speed and distance based on adjusted positions.
• Implement functions to annotate speed and distance in the output video.

Conclusion

• The project encompasses various machine learning and computer vision techniques.
• Emphasizes hands-on experience with real-world problems in sports analysis.
• Final output includes annotated videos displaying player movements, speeds, and acquisition of the ball.
• A comprehensive project that enhances a resume for beginners and experienced machine learning engineers alike.