Dive into a comprehensive tutorial on building a Football AI system to enhance match statistics analysis. Learn to leverage computer vision and machine learning techniques for player tracking, team identification, and advanced metrics calculation like ball possession and speed. Explore the architectural blueprint for Football AI, fine-tune YOLOv8 for object detection, implement robust multi-object tracking with ByteTrack, and perform player clustering using embedding analysis. Master perspective transformation, pitch landmark detection, and homography applications for creating virtual overlays and tactical radar views. Gain insights into spatial analysis for ball territory mapping and discuss implementation challenges. Discover potential future applications in sports analytics while getting hands-on experience with Python-based tools and datasets provided by Roboflow.
Football AI Tutorial: From Basics to Advanced Stats with Python
Overview
Syllabus
- Football Soccer AI: The Next Level
- Architectural Blueprint: Models & Tools for Football AI
- YOLOv8 Fine-Tuning: Optimizing for Football Object Detection
- Deploying YOLOv8 with Inference
- ByteTrack: Robust Multi-Object Tracking
- Embedding Analysis: Clustering Players with SigLIP & UMAP
- Perspective Transformation: Homography Fundamentals
- YOLOv8x-pose Training: Precise Pitch Landmark Detection
- Keypoint Inference: Real-Time Pitch Understanding
- Homography Application: Virtual Lines & Field Overlay
- Top-Down Projection: Creating a Tactical Radar View
- Spatial Analysis: Ball Territory
- Implementation Challenges
- Beyond the Basics: What Else is Possible?
Community Session Aug 29th, 2024 at 08:00 AM PST / 11:00 AM EST / PM CET: https://www.youtube.com/watch?v=Xwou5qO--vY
Taught by
Roboflow
