🚗 Ocean City Parking Lot Occupancy Tracker

A real-time parking lot occupancy tracking system that uses YOLO computer vision to monitor parking spots and provides a web-based dashboard for viewing current availability.

🌟 Features

• Real-time Computer Vision: Uses YOLOv11 model to detect and track parked vehicles
• Live Web Dashboard: Beautiful, responsive web interface with live updates
• REST API: RESTful endpoints for parking status updates and retrieval
• Visual Indicators: Color-coded occupancy bar (green/yellow/red based on capacity)
• Live Camera Feed: Displays live snapshots from the parking lot camera
• Automatic Updates: Real-time status updates every second

🏗️ Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   parkinglot.py │────│     app.py      │────│   index.html    │
│  (CV Processing)│    │  (Flask API)    │    │ (Web Dashboard) │
│                 │    │                 │    │                 │
│ • YOLO Detection│    │ • REST API      │    │ • Live Updates  │
│ • Spot Tracking│    │ • Data Storage  │    │ • Progress Bar  │
│ • API Updates  │    │ • CORS Support  │    │ • Live Feed     │
└─────────────────┘    └─────────────────┘    └─────────────────┘

📁 Project Structure

parking/
├── app.py                      # Flask web server and API
├── parkinglot.py              # Computer vision processing
├── requirements.txt           # Python dependencies
├── README.md                  # Project documentation
├── models/
│   └── yolo11n.pt            # YOLOv11 model weights
├── resources/
│   ├── background.jpg         # Dashboard background image
│   ├── bounding_boxes.json    # Parking spot annotations
│   └── screenshot_to_plot.png # Reference image
├── static/
│   └── resources/
│       └── background.jpg     # Static assets
├── templates/
│   └── index.html            # Web dashboard template
└── screenshots/
    └── tracked_spots_59.png   # Sample output

🚀 Quick Start

Prerequisites

• Python 3.13 or higher
• Virtual environment (recommended)
• Internet connection for live camera feed

Installation

1. Clone the repository

   git clone https://github.com/ptrpetro/ParkingLot_Occupancy.git
   cd parking

2. Set up virtual environment

   python -m venv venv
   # On Windows
   venv\Scripts\activate
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Running the Application

1. Start the Flask web server

   python app.py

   The web dashboard will be available at http://127.0.0.1:5000

2. Start the computer vision processor (in a new terminal)

   python parkinglot.py

3. View the dashboard Open your browser and navigate to http://127.0.0.1:5000

🖥️ Web Dashboard

The web interface provides:

• Live Camera Feed: Real-time snapshots from the Ocean City inlet parking lot
• Occupancy Statistics: Current occupied and available spots
• Visual Progress Bar: Color-coded occupancy indicator
  • 🟢 Green: < 50% occupied
  • 🟡 Yellow: 50-80% occupied
  • 🔴 Red: > 80% occupied
• Last Updated: Timestamp of the most recent data update

🔧 API Endpoints

GET /api/status

Returns the current parking status.

Response:

{
  "occupied": 23,
  "available": 36,
  "last_updated": "2025-07-02 14:30:15"
}

POST /api/update-parking

Updates the parking status (used by the CV processor).

Request Body:

{
  "occupied": 23,
  "available": 36
}

Response:

{
  "status": "success",
  "data": {
    "occupied": 23,
    "available": 36,
    "last_updated": "2025-07-02 14:30:15"
  }
}

🤖 Computer Vision Details

YOLO Model

• Model: YOLOv11 Nano (yolo11n.pt)
• Tracker: BotSORT for improved accuracy
• Detection: Vehicle detection and tracking in parking spots

Parking Spot Configuration

Parking spots are defined in resources/bounding_boxes.json with bounding box coordinates for each monitored parking space.

Video Source

Currently configured to monitor the Ocean City inlet parking lot via live stream:

stream_url = "https://t1.ipcamlive.com/timelapses/5aec8d9905b9a/2025-04-06/video.mp4"

📦 Dependencies

Core Libraries

• Flask 3.1.0: Web framework and API
• Flask-CORS 5.0.0: Cross-origin resource sharing
• OpenCV 4.11.0: Computer vision processing
• Ultralytics 8.3.103: YOLO model implementation
• Requests 2.32.3: HTTP client for API communication

ML/AI Libraries

• PyTorch 2.6.0: Deep learning framework
• NumPy 2.1.1: Numerical computing
• Pandas 2.2.3: Data manipulation

Visualization

• Matplotlib 3.10.1: Plotting and visualization
• Seaborn 0.13.2: Statistical visualization

🎯 Usage Examples

Running with Custom Video Source

Modify the stream_url in parkinglot.py:

stream_url = "your_camera_stream_url_here"

Testing the API

# Get current status
curl http://127.0.0.1:5000/api/status

# Update parking data
curl -X POST http://127.0.0.1:5000/api/update-parking \
  -H "Content-Type: application/json" \
  -d '{"occupied": 25, "available": 34}'

🔧 Configuration

Parking Spot Count

Update the total number of parking spots in parkinglot.py:

parkingSpots = 59  # Total number of parking spots

Tracking Settings

Modify tracking parameters in the ParkingManagement initialization:

parkingmanager = solutions.ParkingManagement(
    model="models/yolo11n.pt",
    json_file="resources/bounding_boxes.json",
    tracker="botsort.yaml"  # or "bytetrack.yaml" for faster processing
)

🐛 Troubleshooting

Common Issues

1. "Flask missing" error

   pip install flask flask-cors

2. Camera stream not accessible

   • Check internet connection
   • Verify the stream URL is accessible
   • Try using a local video file for testing

3. YOLO model not found

   • Ensure models/yolo11n.pt exists
   • Download from Ultralytics if missing

4. Permission errors

   • Ensure virtual environment is activated
   • Check file permissions in the project directory

📄 License

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

🙏 Acknowledgments

• Ultralytics for the YOLO implementation
• Ocean City Live for the parking lot camera feed
• Flask for the web framework
• Tailwind CSS for the responsive UI