Elevator Passenger Abnormal Behavior Recognition Method Based on Digital Twin

Yaohui Song, Xiaolin Li

2024

Abstract

Aiming at the lack of abnormal behavior dataset and scarcity of samples of elevator passengers, a method based on digital twin is proposed by this paper to build a vertical elevator passenger abnormal behavior detection platform and realize the virtual and real mapping of elevator operation status and passenger behavior. The digital twin scene is combined with the theory of human behavior modeling to enhance the abnormal behavior of passengers and provide sufficient abnormal behaviour data sources. In order to solve the problem of confusion between passengers and car background caused by the small range of elevator monitoring and reduce the accuracy of feature extraction, YOLOv7-OpenPose is used by this paper to obtain human bone features, which improves the recognition accuracy on the premise of ensuring the recognition speed, and realizes the rapid recognition of passengers' abnormal behaviors fused with twin data. Experimental results show that the proposed method not only demonstrates the feasibility, efficiency and security of digital twin technology in the creation of abnormal data, but also reflects the superiority of the improved algorithm in pose recognition.

Paper Citation

in Harvard Style

Song Y. and Li X. (2024). Elevator Passenger Abnormal Behavior Recognition Method Based on Digital Twin. In Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS; ISBN 978-989-758-715-3, SciTePress, pages 87-93. DOI: 10.5220/0012887900004536

in Bibtex Style

@conference{dmeis24,
author={Yaohui Song and Xiaolin Li},
title={Elevator Passenger Abnormal Behavior Recognition Method Based on Digital Twin},
booktitle={Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS},
year={2024},
pages={87-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012887900004536},
isbn={978-989-758-715-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Data Mining, E-Learning, and Information Systems - Volume 1: DMEIS
TI - Elevator Passenger Abnormal Behavior Recognition Method Based on Digital Twin
SN - 978-989-758-715-3
AU - Song Y.
AU - Li X.
PY - 2024
SP - 87
EP - 93
DO - 10.5220/0012887900004536
PB - SciTePress