Design and Development of Elevator Assistance Based on Human Traffic

Elevator traffic across multiple floors needs to be controlled and prioritized based on the emergency of people. A computer vision-based elevator is proposed in this paper, where the elevator is designed using reconfigurable based control and traffic is analyzed using computer vision algorithms. The computer vision algorithms such as KNN algorithm, background subtraction, masking, and binarization are used to determine the age and analyze the traffic. Moreover, assigning the priorities on the basis of traffic to the required floor makes the elevator accessible to everyone and to make the inter floor transportation smoother and easier. The architecture is implemented using Raspberry Pi for analyzing traffic at various floors, and the elevator is controlled with FPGA and stepper motors. The processing of the proposed model works on Visual Studio Code and Xilinx Vivado 2017.3 with an accuracy of 95%. SPI protocol is used to communicate between Raspberry Pi and FPGA.