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SkalskiP

sports

Cool experiments at the intersection of Computer Vision and Sports βš½πŸƒ.

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Created 2022-12-14 Β· Updated 2026-10-08 Β· #17058 today
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SkalskiP/sports is ranked 17,058 with 558 stars.

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SkalskiP/sports is ranked 17,058 among these 8 peers; PacktPublishing/Building-Agentic-AI-Systems is ranked 17,057.

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Key parameters and specifications
Key parameters and specifications
ParameterValue
LanguageJupyter Notebook
CategoryTutorials
SubcategoryTool-specific tutorials
KeywordsComputer vision
Stars558
Forks41
Contributors1
Created date2022-12-14
Updated date2026-10-08
Stars in 1 day0
Stars in 7 days0
Rank17,058
Previous rank16,850
Rank change-208
7-day stars rank15,885
Previous 7-day stars rank17,910
7-day stars rank change2,025
Total stars rank17,780
Previous total stars rank17,747
Total stars rank change-33
README summary

⚽ Football Players Tracking with YOLOv5 + ByteTrack I have long been fascinated by the use of Computer Vision in sports. After all, it is a combination of two things I love. FIFA World Cup 2022 has motivated me to revisit this idea. This time I used a combination of YOLOv5 and ByteTrack to track football players on the field. This blog post accompanies the Roboflow video where I talk through how to track players on a football field. 🀸 3D Football Players

Pose Estimation with YOLOv7 I was watching a FIFA 2022 World Cup match the other day, and one of the things that caught my eye was VAR - Video Assistant Referee, or to be more precise, the part of it responsible for analyzing whether a player was on the offside. I did a little research and found that the system performs pose estimation on multiple cameras at once. I decided to check how difficult it would be to reproduce it at

home using two cameras and YOLOv7. πŸ‘• Assigning Football Players to Teams by Uniform Color with GPT-4V This project explores the use of GPT-4V in sports analytics, specifically in the context of football. Its primary aim was to evaluate whether GPT-4V could effectively distinguish and assign players to teams based on the color of their uniforms. This was achieved through the implementation of several advanced vision prompting techniques.

Frequently asked questions

How many stars and forks are recorded?

SkalskiP/sports has 558 stars and 41 forks.

What is the repository rank?

SkalskiP/sports is ranked 17,058.

What language and category are recorded?

SkalskiP/sports has 558 stars, uses Jupyter Notebook and belongs to Tutorials.

Which repository is closest in rank?

SkalskiP/sports is compared with PacktPublishing/Building-Agentic-AI-Systems, ranked 17,057 with 558 stars.

README

⚽ Football Players Tracking with YOLOv5 + ByteTrack

I have long been fascinated by the use of Computer Vision in sports. After all, it is a combination of two things I love. Almost three years ago, I wrote a post on my personal blog in which I triedβ€Šβ€”β€Šat that time, still using YOLOv3β€Šβ€”β€Što detect and classify basketball players on the court.

FIFA World Cup 2022 has motivated me to revisit this idea. This time I used a combination of YOLOv5 and ByteTrack to track football players on the field. This blog post accompanies the Roboflow video where I talk through how to track players on a football field.

https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4

🀸 3D Football Players Pose Estimation with YOLOv7

I was watching a FIFA 2022 World Cup match the other day, and one of the things that caught my eye was VAR - Video Assistant Referee, or to be more precise, the part of it responsible for analyzing whether a player was on the offside. I did a little research and found that the system performs pose estimation on multiple cameras at once. I decided to check how difficult it would be to reproduce it at home using two cameras and YOLOv7.

https://user-images.githubusercontent.com/26109316/207677038-20f951a6-e469-4b3f-a934-66e036fcff69.mp4

πŸ‘• Assigning Football Players to Teams by Uniform Color with GPT-4V

This project explores the use of GPT-4V in sports analytics, specifically in the context of football. Its primary aim was to evaluate whether GPT-4V could effectively distinguish and assign players to teams based on the color of their uniforms. This was achieved through the implementation of several advanced vision prompting techniques.

https://github.com/SkalskiP/sports/assets/26109316/b354b38d-1a12-477d-9283-45059ce12467

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