🚦 AI Traffic Monitoring System

A real-time Computer Vision system for intelligent traffic monitoring, vehicle detection, tracking, and congestion analysis — built for smart city infrastructure.

Features • Demo • Installation • Usage • Architecture • Roadmap

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📸 Demo

Upload your own traffic video and watch the AI detect, track, and count vehicles in real-time.

🚗 Vehicle detected → 🔢 Unique ID assigned → 📊 Stats updated → 🚦 Dashboard live

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✨ Features

Feature	Description	Status
🎯 Real-time Detection	YOLOv8 Nano for fast, accurate vehicle detection	✅ Live
🔢 Multi-Object Tracking	ByteTrack assigns persistent IDs across frames	✅ Live
📊 Vehicle Counting	Counts vehicles crossing a virtual tripwire	✅ Live
🚗 Vehicle Classification	Detects cars, trucks, buses, motorcycles	✅ Live
📈 Live Dashboard	Real-time Streamlit web interface with metrics	✅ Live
🎥 Webcam Support	Works with live webcam feed	✅ Live
⚡ FPS Monitoring	Real-time performance tracking	✅ Live
🚑 Emergency Detection	Ambulance/police vehicle prioritization	🔄 Coming Soon
🗺️ Heatmap Analysis	Traffic density heatmaps per lane	🔄 Coming Soon
☁️ Cloud Deployment	AWS/Railway deployment ready	🔄 Coming Soon

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🏗️ Architecture

📁 TrafficMonitor/
├── 📄 app.py               # Streamlit web dashboard (Frontend)
├── 📄 detector.py          # YOLOv8 detection engine (Backend)
├── 📄 requirements.txt     # Python dependencies
├── 📄 README.md            # You are here
└── 🎥 traffic.mp4          # Sample test video

Tech Stack

┌─────────────────────────────────────────────────┐
│                  USER BROWSER                   │
│              (Streamlit Dashboard)               │
└──────────────────────┬──────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────┐
│               STREAMLIT SERVER                  │
│                  (app.py)                       │
└──────────────────────┬──────────────────────────┘
                       │
┌──────────────────────▼──────────────────────────┐
│             DETECTION ENGINE                    │
│               (detector.py)                     │
│                                                 │
│   ┌──────────┐  ┌───────────┐  ┌────────────┐  │
│   │ YOLOv8n  │→ │ ByteTrack │→ │  OpenCV    │  │
│   │Detection │  │ Tracking  │  │ Annotation │  │
│   └──────────┘  └───────────┘  └────────────┘  │
└─────────────────────────────────────────────────┘

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⚙️ Installation

Prerequisites

• Python 3.9 or higher
• MacOS / Linux / Windows
• 4GB RAM minimum (8GB recommended)

Step 1: Clone the Repository

git clone https://github.com/YOUR_USERNAME/Computer-Vision-System-for-Traffic-MonitoringTrafficMonitor.git
cd TrafficMonitor

Step 2: Create Virtual Environment

python3 -m venv traffic_env
source traffic_env/bin/activate     # MacOS/Linux
# traffic_env\Scripts\activate      # Windows

Step 3: Install Dependencies

pip install --upgrade pip
pip install -r requirements.txt

Step 4: Download Sample Video (Optional)

curl -L -o traffic.mp4 "https://videos.pexels.com/video-files/5834623/5834623-hd_1920_1080_25fps.mp4"

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🚀 Usage

Run the Web Dashboard

streamlit run app.py

Then open your browser at: http://localhost:8501

Run Detection Engine Directly

python3 detector.py

Dashboard Controls

1. Upload Video — Upload any MP4 traffic video from the sidebar
2. Select Source — Choose between video file or live webcam
3. Click Start — Begin real-time AI analysis
4. Monitor Stats — Watch live FPS, vehicle counts, and crossing data

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📊 How It Works

1. Detection (YOLOv8)

YOLOv8 Nano scans each video frame and draws bounding boxes around detected vehicles. It classifies them into:

• 🚗 Car
• 🚌 Bus
• 🚛 Truck
• 🏍️ Motorcycle

2. Tracking (ByteTrack)

Each detected vehicle gets a unique persistent ID (#1, #2, #3...). ByteTrack is smart enough to keep the same ID even if a vehicle temporarily disappears behind another object (occlusion).

3. Counting (Virtual Tripwire)

A virtual line is drawn across the road. Every time a vehicle crosses this line, the IN or OUT counter increments depending on direction of travel.

4. Dashboard (Streamlit)

All data streams live into a professional web dashboard showing:

• Live annotated video feed
• Active vehicle count
• IN / OUT crossing totals
• Real-time FPS

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📦 Dependencies

ultralytics==8.1.0        # YOLOv8 object detection
opencv-python==4.9.0      # Video processing
streamlit==1.30.0         # Web dashboard
supervision==0.18.0       # Tracking & annotation utilities
numpy==1.26.0             # Numerical computing
pandas==2.1.0             # Data handling

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🗺️ Roadmap

• Real-time vehicle detection
• Multi-object tracking with unique IDs
• Vehicle counting (IN/OUT)
• Live Streamlit dashboard
• Webcam support
• Speed estimation (km/h)
• Lane-specific counting
• Emergency vehicle alerts (ambulance priority)
• Traffic density heatmaps
• Congestion alerts
• REST API endpoints
• Docker containerization
• AWS/Railway deployment
• Indian traffic dataset fine-tuning (rickshaws, autos)

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🤝 Contributing

Contributions are welcome! Here's how:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/SpeedEstimation)
3. Commit your changes (git commit -m 'Add speed estimation')
4. Push to the branch (git push origin feature/SpeedEstimation)
5. Open a Pull Request

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👤 Author

*Debadri Das
Third-year CS Student | AI/ML Enthusiast 📍 Kolkata, India

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📄 License

This project is licensed under the MIT License — see the LICENSE file for details.

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🙏 Acknowledgements

• Ultralytics YOLOv8 — Object detection model
• Supervision — CV utility library by Roboflow
• Streamlit — Web dashboard framework
• ByteTrack — Multi-object tracking algorithm

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⭐ If this project helped you, please give it a star!

Built with ❤️ for smart city infrastructure and Indian traffic management