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abdullahtarek

football_analysis

This repository contains a comprehensive computer vision/machine learning football project that uses YOLO for object detection, Kmeans for pixel segmentation, optical flow for motion tracking, and perspective transformation to analyze player movements in football videos

TutorialsML/AI fundamentalsJupyter Notebook
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创建于 2024-04-18 · 更新于 2026-10-14 · 今日第 12053 名
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abdullahtarek/football_analysis 排名第 12,053，有 1,019 个星标。

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abdullahtarek/football_analysis 排名第 12,053，此处有 8 个同类条目；itsdouges/typescript-transformer-handbook 排名第 12,049。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Tutorials
子类别ML/AI fundamentals
星标数1,019
分叉数343
贡献者1
创建日期2024-04-18
更新日期2026-10-14
1 天星标数0
7 天星标数3
排名12,053
上次排名11,969
排名变化-84
7 天星标排名7,108
上次 7 天星标排名7,138
7 天星标排名变化30
总星标排名11,004
上次总星标排名10,982
总星标排名变化-22
国家Egypt
README 摘录

Football Analysis Project Introduction The goal of this project is to detect and track players, referees, and footballs in a video using YOLO, one of the best AI object detection models available. We will also train the model to improve its performance. Additionally, we will assign players to teams based on the colors of their t-shirts using Kmeans for pixel segmentation and clustering. With this information, we can measure a team's ball acquisition percentage in a match. We will

also use optical flow to measure camera movement between frames, enabling us to accurately measure a player's movement. Furthermore, we will implement perspective transformation to represent the scene's depth and perspective, allowing us to measure a player's movement in meters rather than pixels. Finally, we will calculate a player's speed and the distance covered. This project covers various concepts and addresses real-world problems, making it suitable for both beginners and experienced machine learning engineers. Modules Used The following modules

are used in this project: YOLO: AI object detection model Kmeans: Pixel segmentation and clustering to detect t-shirt color Optical Flow: Measure camera movement Perspective Transformation: Represent scene depth and perspective Speed and distance calculation per player Trained Models Trained Yolo v5 Sample video Sample input video Requirements To run this project, you need to have the following requirements installed: Python 3. x ultralytics supervision OpenCV NumPy Matplotlib Pandas

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记录了多少星标和分叉？

abdullahtarek/football_analysis 有 1,019 个星标和 343 个分叉。

仓库排名是多少？

abdullahtarek/football_analysis 排名第 12,053。

记录的语言和类别是什么？

abdullahtarek/football_analysis 有 1,019 个星标，语言为 Jupyter Notebook，类别为 Tutorials。

哪个仓库排名最接近？

abdullahtarek/football_analysis 与 itsdouges/typescript-transformer-handbook 比较，后者排名第 12,049，有 1,020 个星标。

README

Football Analysis Project

Introduction

The goal of this project is to detect and track players, referees, and footballs in a video using YOLO, one of the best AI object detection models available. We will also train the model to improve its performance. Additionally, we will assign players to teams based on the colors of their t-shirts using Kmeans for pixel segmentation and clustering. With this information, we can measure a team's ball acquisition percentage in a match. We will also use optical flow to measure camera movement between frames, enabling us to accurately measure a player's movement. Furthermore, we will implement perspective transformation to represent the scene's depth and perspective, allowing us to measure a player's movement in meters rather than pixels. Finally, we will calculate a player's speed and the distance covered. This project covers various concepts and addresses real-world problems, making it suitable for both beginners and experienced machine learning engineers.

Modules Used

The following modules are used in this project:

  • YOLO: AI object detection model
  • Kmeans: Pixel segmentation and clustering to detect t-shirt color
  • Optical Flow: Measure camera movement
  • Perspective Transformation: Represent scene depth and perspective
  • Speed and distance calculation per player

Trained Models

Sample video

Requirements

To run this project, you need to have the following requirements installed:

  • Python 3.x
  • ultralytics
  • supervision
  • OpenCV
  • NumPy
  • Matplotlib
  • Pandas
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