SkalskiP/sports
Jupyter notebooks demonstrating football player detection, tracking, and 3D pose estimation using YOLOv5, YOLOv7, and ByteTrack with PyTorch.
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This repository contains Jupyter notebook tutorials showcasing computer vision applications in sports analytics. It demonstrates player tracking using YOLOv5 combined with ByteTrack for multi-object tracking, and 3D pose estimation using YOLOv7 for analyzing player positioning. The experiments are applied to football/soccer footage for applications like VAR offside analysis.
Frequently asked
- What is SkalskiP/sports?
- Jupyter notebooks demonstrating football player detection, tracking, and 3D pose estimation using YOLOv5, YOLOv7, and ByteTrack with PyTorch.
- Is sports open source?
- Yes — SkalskiP/sports is an open-source project tracked on heatdrop.
- What language is sports written in?
- SkalskiP/sports is primarily written in Jupyter Notebook.
- How popular is sports?
- SkalskiP/sports has 552 stars on GitHub.
- Where can I find sports?
- SkalskiP/sports is on GitHub at https://github.com/SkalskiP/sports.