/Football-Analytics-with-Deep-Learning-and-Computer-Vision

Primary LanguageJupyter Notebook

Football Analytics with Deep Learning and Computer Vision

Project goal: Create a web application to automate football analysis, and provide useful information that helps in decision making.

Current stage: Developing a streamlit web application for football object detection with tactical map representation.

Installation & How to use?

Steps:

  1. Clone the repository using the command git clone https://github.com/Hmzbo/Football-Analytics-with-Deep-Learning-and-Computer-Vision.git
  2. Install the required libraries listed in the file requirement.txt, you can use the command conda env create -f environment.yml to create the conda env I use but make sure the pytorch installation is compatible with your machine.
  3. Use the command steamlit run main.py to start the application.

Features

  • Detect players, referees and ball.
  • Predict players teams based on predefined team colors.
  • Build a tactical map representation.
  • Track ball movements.

Application Workflow

The journey of the input video and different functionalities are illustrated in the workflow diagram below.

workflow diagram