Football Analysis System

A comprehensive computer vision system for analyzing football matches using advanced object detection and tracking techniques. This project leverages YOLO (You Only Look Once) for object detection and ByteTrack for multi-object tracking to identify and track players, referees, and the ball throughout video footage.

🎯 Features

• Real-time Object Detection: Detects players, goalkeepers, referees, and the ball using YOLO11
• Multi-Object Tracking: Maintains consistent player identities across frames using ByteTrack
• Visual Annotations:
  • Players: Red ellipses with unique ID numbers
  • Referees: Yellow ellipses
  • Ball: Green triangular markers
• Batch Processing: Efficient frame processing with configurable batch sizes
• Caching System: Save and load detection results to speed up subsequent analyses
• Video Output: Generate annotated video files with tracking overlays

🚀 Demo

Example of the system tracking players, referees, and ball in real-time

📋 Requirements

ultralytics
opencv-python
supervision
numpy
pickle-mixin

🛠️ Installation

1. Clone the repository

   https://github.com/AmmarMohamed0/Football-Analysis-System.git
   cd Football-Analysis-System

2. Create a virtual environment (recommended)

   python -m venv football_env
   source football_env\Scripts\activate  

3. Install dependencies

   pip install -r requirements.txt

4. Download YOLO model

   # The system will automatically download yolo11l.pt on first run

🎮 Usage

Basic Usage

1. Place your video file in the input_videos/ directory
2. Run the analysis:

   python main.py

3. Find the output in the output_videos/ directory

🧠 How It Works

1. Video Input: Load video frames using OpenCV
2. Object Detection: YOLO model detects players, referees, goalkeepers, and ball
3. Class Mapping: Goalkeepers are mapped to player class for unified tracking
4. Multi-Object Tracking: ByteTrack maintains object identities across frames
5. Visualization: Draw ellipses for people, triangles for ball with unique IDs
6. Output Generation: Create annotated video with tracking overlays

🎯 Training Your Own YOLO Model

To train your own YOLO model for football analysis using the Roboflow dataset:

1. Prepare Environment

Set up your training environment in Google Colab (Football_Analysis_System.ipynb):

# Install required packages
!pip install ultralytics --quiet
!pip install roboflow --quiet

import ultralytics
ultralytics.checks()
from ultralytics import YOLO
from roboflow import Roboflow

2. Download Dataset

Access the football players detection dataset from Roboflow:

# Initialize Roboflow with your API key
rf = Roboflow(api_key="YOUR_API_KEY_HERE")
project = rf.workspace("roboflow-jvuqo").project("football-players-detection-3zvbc")
version = project.version(1)
dataset = version.download("yolov11")

Dataset Source: Football Players Detection Dataset by Roboflow

3. Train Model

Train the YOLO model with the downloaded dataset:

# Train the model using command line interface
!yolo task=detect mode=train data={dataset.location}/data.yaml model="yolo11l.pt" epochs=100 imgsz=640

Training Parameters:

• Model: YOLOv11 Large (yolo11l.pt)
• Epochs: 100
• Image Size: 640x640
• Task: Object Detection

4. Replace Model

After training completes:

1. Locate your trained model (usually in runs/detect/train/weights/best.pt)
2. Replace the existing models/best.pt with your newly trained model
3. Update any model paths in your configuration if necessary

5. Dataset Information

The dataset includes annotations for:

• Players
• Referees
• Goalkeepers
• Ball detection

Note: Make sure to replace "YOUR_API_KEY_HERE" with your actual Roboflow API key before running the training script.

📊 Performance

• Detection Accuracy: Depends on custom model training
• Tracking Consistency: ByteTrack maintains 95%+ identity consistency
• Processing Speed: ~30 FPS on RTX 3080 (batch processing)
• Memory Usage: ~4GB VRAM for 1080p video processing

🙏 Acknowledgments

• YOLO: Ultralytics for object detection
• ByteTrack: Multi-object tracking algorithm
• Supervision: Computer vision utilities
• OpenCV: Image processing capabilities

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