🏢 Intelligent Lift Management (ILM)

Intelligent Lift Management (ILM) is a computer vision-based system designed to revolutionize elevator operations in modern buildings. By leveraging the YOLO (You Only Look Once) object detection model for real-time human detection and a sophisticated priority-scoring algorithm, ILM minimizes wait times, improves accessibility, and maximizes the overall efficiency of lift dispatches.

✨ Key Features

• 👁️ Real-Time Human Detection: Utilizes YOLO to accurately detect the presence and count of individuals waiting for an elevator in real-time.
• 🧠 Priority-Scoring Algorithm: Intelligently calculates optimal lift routing and dispatching based on crowd density, reducing bottlenecks during peak hours.
• ♿ Accessibility Enhancements: Ensures prioritized service and equitable lift distribution for heavily populated floors.
• 🖥️ Interactive Dashboard: Features a clean HTML/CSS frontend to visualize lift status and crowd metrics.
• 🏗️ 3D Modeling: Includes 3D visualizations and test environments to simulate and optimize real-world elevator traffic scenarios.

🛠️ Tech Stack

• Computer Vision: Python, YOLO
• Backend: Python
• Frontend: HTML5, CSS3
• Automation/Scripting: Windows Batchfile (.bat)

📂 Project Structure

Intelligent-Lift-Management/
├── 3D model/               # 3D assets and environment models
├── backend_v0.0.1/         # Backend server logic (Legacy)
├── backend_v0.0.2/         # Backend server logic (Stable)
├── backend_v0.0.3/         # Backend server logic (Current/Experimental)
├── frontend/               # HTML/CSS user interface files
├── model/                  # YOLO model weights and configuration
├── test_images/            # Sample imagery for testing the CV model
├── ILM.bat                 # Batch script for quick startup
├── idea_present.pptx       # Presentation slides detailing the ILM concept
├── requirements.txt        # Python dependencies
└── .gitignore              # Git ignore rules

🚀 Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

Ensure you have the following installed:

• Python 3.8+
• Git

Installation

1. Clone the repository:

   git clone https://github.com/mohanevs/Intelligent-Lift-Management.git
   cd Intelligent-Lift-Management

2. Install the required dependencies: It is recommended to use a virtual environment.

   pip install -r requirements.txt

3. Configure the Model: Ensure the necessary YOLO weight files are placed correctly inside the model/ directory.

Execution

You can quickly launch the system using the provided batch script (Windows only):

ILM.bat

Alternatively, navigate to the latest backend directory and start the Python server manually.

📈 Future Enhancements

• Integration with IoT sensors for multi-modal data fusion.
• Cloud deployment capabilities for centralized building management.
• Advanced predictive analytics using historical traffic data.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page if you want to contribute.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is open-source. Please refer to the repository for specific licensing details.