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https://github.com/SkalskiP/sports
Cool experiments at the intersection of Computer Vision and Sports ⚽🏃
https://github.com/SkalskiP/sports
computer-vision deep-learning deep-neural-networks gpt-4 gpt-4-vision object-detection prompt-engineering pytorch sports-analytics tutorial yolov5 yolov7
Last synced: 3 months ago
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Cool experiments at the intersection of Computer Vision and Sports ⚽🏃
- Host: GitHub
- URL: https://github.com/SkalskiP/sports
- Owner: SkalskiP
- Created: 2022-12-14T16:58:14.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2023-12-12T11:10:48.000Z (7 months ago)
- Last Synced: 2024-01-25T05:11:04.715Z (6 months ago)
- Topics: computer-vision, deep-learning, deep-neural-networks, gpt-4, gpt-4-vision, object-detection, prompt-engineering, pytorch, sports-analytics, tutorial, yolov5, yolov7
- Language: Jupyter Notebook
- Homepage:
- Size: 15.1 MB
- Stars: 415
- Watchers: 11
- Forks: 27
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
Lists
- awesome-stars - SkalskiP/sports - Cool experiments at the intersection of Computer Vision and Sports ⚽🏃 (Jupyter Notebook)
README
# ⚽ Football Players Tracking with YOLOv5 + ByteTrack
[](https://www.youtube.com/watch?v=QCG8QMhga9k)
[](https://blog.roboflow.com/track-football-players/)
[](https://github.com/roboflow-ai/notebooks/blob/main/notebooks/how-to-track-football-players.ipynb)
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 tried — at that time, still using YOLOv3 — to [detect and classify basketball players on the court](https://towardsdatascience.com/chess-rolls-or-basketball-lets-create-a-custom-object-detection-model-ef53028eac7d).
FIFA World Cup 2022 has motivated me to revisit this idea. This time I used a combination of [YOLOv5](https://github.com/ultralytics/yolov5) and [ByteTrack](https://github.com/ifzhang/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
[](https://www.youtube.com/watch?v=AWjKfjDGiYE)
[](https://github.com/SkalskiP/sport/tree/master/football-players-pose-estimation)
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](https://www.youtube.com/watch?v=WycjDx6giVE) 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://github.com/WongKinYiu/yolov7).
https://user-images.githubusercontent.com/26109316/207677038-20f951a6-e469-4b3f-a934-66e036fcff69.mp4
# 👕 Assigning Football Players to Teams by Uniform Color with GPT-4V
[](https://github.com/SkalskiP/sport/tree/master/football-analysis-with-gpt4-vision)
This project explores the use of [GPT-4V](https://openai.com/research/gpt-4v-system-card) 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