DFL - Bundesliga Data Shootout Analysis using YOLO, OpenCV, and Python

This project takes works on the You Only Look Once (YOLO) object detection algorithm, OpenCV, and Python to analyze data from the DFL - Bundesliga Data Shootout. The primary aim is to detect and track soccer players and the ball in video footage.

Table of Contents

• Introduction
• Features
• Requirements
• Installation
• Usage
• References

Introduction

The project is based on a tutorial video on YouTube which demonstrates how to utilize YOLO, OpenCV, and Python for sports analysis. The DFL - Bundesliga Data Shootout provides a rich dataset ideal for testing and developing computer vision models for sports analytics.

Features

• Object Detection: Detect players and ball in the video.
• Tracking: Track the movement of detected objects across frames.
• Analysis: Perform analysis on the detected objects to gain insights.

Requirements

• Python 3.x+
• OpenCV
• NumPy
• Matplotlib
• Pandas
• Ultralytics
• Supervision
• YOLOv3 and YOLOv5 weights and configuration files

Installation

Clone the Repository

git clone https://github.com/aaditya29/DFL---Bundesliga-Data-Shootout-Analysis.git
cd DFL---Bundesliga-Data-Shootout-Analysis

Usage

Download YOLO Weights and Configurations

Download the YOLOv5 weights and configuration files from the official YOLO website or from the tutorial video description.

References

This project is inspired by the following YouTube tutorial video:

• YOLO Object Detection using OpenCV and Python