Edge-Computing-Based People-Counting System for Elevators Using MobileNet–Single-Stage Object Detection

Tsu-Chuan Shen and Edward T.-H. Chu ()
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Tsu-Chuan Shen: Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan
Edward T.-H. Chu: Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 640301, Taiwan

Future Internet, 2023, vol. 15, issue 10, 1-21

Abstract: Existing elevator systems lack the ability to display the number of people waiting on each floor and inside the elevator. This causes an inconvenience as users cannot tell if they should wait or seek alternatives, leading to unnecessary time wastage. In this work, we adopted edge computing by running the MobileNet–Single-Stage Object Detection (SSD) algorithm on edge devices to recognize the number of people inside an elevator and waiting on each floor. To ensure the accuracy of people counting, we fine-tuned the SSD parameters, such as the recognition frequency and confidence thresholds, and utilized the line of interest (LOI) counting strategy for people counting. In our experiment, we deployed four NVIDIA Jetson Nano boards in a four-floor building as edge devices to count people when they entered specific areas. The counting results, such as the number of people waiting on each floor and inside the elevator, were provided to users through a web app. Our experimental results demonstrate that the proposed method achieved an average accuracy of 85% for people counting. Furthermore, when comparing it to sending all images back to a remote server for people counting, the execution time required for edge computing was shorter, without compromising the accuracy significantly.

Keywords: indoor people counting; object tracking; image recognition; edge computing; Internet of Things (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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