This repository showcases experimental computer vision techniques applied to sports analytics, specifically football. It targets researchers and developers interested in sports tracking, pose estimation, and player identification, offering practical implementations and insights into leveraging modern CV models for sports-related tasks.

How It Works

The project demonstrates three distinct applications: player tracking using YOLOv5 and ByteTrack for robust object association, 3D pose estimation with YOLOv7 to analyze player movements and offside detection, and team assignment via uniform color analysis leveraging GPT-4V with advanced prompting techniques. This multi-faceted approach highlights the versatility of current CV models in addressing complex sports scenarios.

Quick Start & Requirements

• Installation: Typically involves cloning the repository and installing Python dependencies via pip. Specific model weights (YOLOv5, YOLOv7) and potentially datasets are required.
• Prerequisites: Python 3.x, PyTorch, OpenCV, and potentially CUDA for GPU acceleration. GPT-4V integration requires API access.
• Resources: Performance is dependent on the chosen models and hardware; GPU acceleration is recommended for real-time applications.
• Links: Roboflow video, GPT-4V demo

Highlighted Details

• Football player tracking with YOLOv5 and ByteTrack.
• 3D football player pose estimation using YOLOv7.
• Team assignment by uniform color with GPT-4V.
• Demonstrates practical applications of state-of-the-art CV models in sports.

Maintenance & Community

The project appears to be a personal exploration by SkalskiP, with no explicit mention of a broader community, active development team, or roadmap.

Licensing & Compatibility

The repository's license is not explicitly stated in the provided README. Users should verify licensing for any commercial or closed-source integration.

Limitations & Caveats

The project focuses on experimental applications and may not represent production-ready solutions. Specific performance benchmarks or robustness guarantees are not detailed. Integration with GPT-4V requires external API access and associated costs.