STAMPede Detection System

🚀 Quick Start

# Install dependencies
pip install -r requirements.txt

# Start the system
python start_enhanced_system.py

📋 System Requirements

• Python 3.8+
• NVIDIA GPU with CUDA support (recommended)
• 8GB+ RAM
• Webcam or video files for testing

🎯 Features

• Real-time Detection: YOLOv8 Large model with GPU acceleration
• Professional Interface: Clean web dashboard with live metrics
• Advanced Analytics: Multi-factor risk assessment and crowd flow analysis
• Simple Visualization: Clear dots instead of cluttered bounding boxes
• Smart Alerts: Conservative thresholds prevent false alarms

📁 Project Structure

person-detection/
├── web_server.py              # Main web application
├── stampede.py                # Core detection algorithm  
├── start_enhanced_system.py   # System startup script
├── train.py                   # Model training script
├── templates/
│   └── index.html            # Web interface
├── requirements.txt           # Dependencies
└── FINAL_PROJECT_DOCUMENTATION.md  # Complete documentation

🔧 Configuration

The system automatically detects and uses GPU acceleration if available. Key parameters:

• Confidence: 0.15 (optimized for dense crowds)
• Image Size: 1280px (high resolution)
• Grid Resolution: 32x24 (fine analysis)
• Risk Thresholds: Conservative to prevent false alarms

📖 Documentation

See FINAL_PROJECT_DOCUMENTATION.md for complete technical details, academic Q&A, and implementation specifics.

🎓 Academic Use

This project demonstrates:

• Computer vision applications
• Real-time processing systems
• GPU acceleration techniques
• Web application development
• Risk assessment algorithms

Perfect for computer science, engineering, and AI/ML courses.