Computer vision-based intelligent elevator information system for efficient demand-based operation and optimization

Abstract

Occupancy and behavior in buildings are considered to be key factors influencing energy consumption. Residential areas contribute substantially to energy consumption in urban environments and have more regular users. Developing methods that can monitor building space utilization patterns and meet occupant comfort requirements is essential for urban energy efficiency. In this work, we propose an innovative computer vision-based Intelligent Elevator Information System (IEIS), which is based on a pipeline framework that sequentially implements elevator door state judgment, passenger attribute recognition, and duplicate-free traffic counting through re-recognition in order to give demand-based operational strategies and recommendations. By monitoring the elevator occupancy on a typical weekday, the elevator operation pattern is classified into three periods including peak period, idle period and balance period, and the demand-driven operation optimization is guided based on their corresponding characteristics. Experiments show that the proposed deep learning approach exhibits high accuracy in all stages of the pipeline. IEIS can facilitate energy-efficient management of elevators, ensure safety, provide human-centered services, and reduce the cost of human intervention while achieving efficient operation, and is expected to migrate to other scenario applications, bringing multiple potential benefits to the society.