Smart Elevator Control and Management

The Smart elevator system that adapts elevator stops according to actual occupancy by people in real-time manner. In our proposed system, computer vision and object detection techniques are incorporated in determining whether people are standing at a floor prior to a stop. An image is taken by the system using a camera that takes a view of the elevator lobby herein recognition of persons is done using machine learning algorithms to make requisite decisions of the elevator's halt. If there are people outside the elevator, a door will open to admit them otherwise the elevator simply skips the floor making movement faster and reduce the travel time. The recommendation decision draws on heuristic image recognition computations like YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector). By using a camera to monitor the outside area, the system makes a binary decision to pull the emergency brake and open the door or to proceed with the next desired floor. This project sets a foundation for future improvements in smart elevator systems, leveraging IoT integration, advanced machine learning models, and cloudbased real-time data processing to further enhance the intelligence and responsiveness of the system.