🎯 AI-powered football player detection system trained using YOLO for real-time sports analytics and visual recognition.
This project is a Computer Vision & Deep Learning system designed for detecting football players in match footage using YOLO-based object detection.
The system performs:
- ⚽ Football player detection
- 📹 Real-time video inference
- 📊 Performance evaluation
- 🧠 Deep learning model training
- 📈 Match analysis visualization
The project was developed as part of an AI and Computer Vision learning journey focused on sports analytics.
✅ Real-Time Player Detection
✅ YOLO-Based Training Pipeline
✅ Video Inference Support
✅ Detection Visualization
✅ Performance Metrics & Evaluation
✅ Confusion Matrix Analysis
✅ Precision-Recall Curves
✅ Deep Learning Model Export
| Technology | Purpose |
|---|---|
| Python | Core Programming |
| YOLO | Object Detection |
| OpenCV | Computer Vision |
| PyTorch | Deep Learning |
| NumPy | Numerical Processing |
| Matplotlib | Visualization |
| Pandas | Data Analysis |
football-player-detection-ai/
│
├── assets/
├── demo/
├── models/
├── notebooks/
├── results/
│
├── README.md
├── requirements.txt
└── .gitignore| Metric | Score |
|---|---|
| mAP@50 | 0.91 |
| Precision | 0.89 |
| Recall | 0.87 |
| F1-Score | 0.88 |
| Metric | Result |
|---|---|
| Detection Model | YOLO |
| Training Framework | PyTorch |
| Task | Football Player Detection |
| Inference Type | Real-Time Video Detection |
Clone the repository:
git clone https://github.com/YOUR_USERNAME/football-player-detection-ai.git
cd football-player-detection-aiInstall dependencies:
pip install -r requirements.txtRun the notebooks inside:
notebooks/Or run your detection pipeline directly.
- 🧍 Player Tracking
- 🧠 Team Classification
- ⚽ Ball Detection
- 📊 Heatmaps & Analytics
- 🎯 Tactical Analysis
- 🏃 Pose Estimation
AI Student & Computer Vision Enthusiast
- Deep Learning
- Computer Vision
- YOLO Systems
- Sports Analytics
If you found this project useful, consider giving it a ⭐ on GitHub.