@inproceedings{db7c9625fcef42508c50b0ec7d709eae,
title = "Recognition of Abnormal Human Behavior in Elevators based on CNN",
abstract = "This study explores a CNN based model to identify abnormal behavior in elevator cabs, which I have named S-LRCN (S-Long-term Recurrent Convolutional Network). It starts with the detection of key points of the human skeleton by using the Openpose method, then further detects and tracks the human body through the CenterNet and DeepSort methods, and finally integrates the Long Short Term Memory Network (LSTM) and Convolutional Neural Network (CNN) to form a deep learning model. In this study, a large dataset (500 video clips) collected from real elevator cabs with different backgrounds has been applied to ensure the robustness and generalizability of the proposed model. At last, this study applies the two mainstream dangerous human behaviors, i.e., door blocking and door picking as case studies to test and evaluate the usability and availability. Experimental results show that the model has a 85% recognition rate of abnormal behavior.",
keywords = "Abnormal behavior, CNN, Door blocking, Door picking, Elevator cabs",
author = "Yajing Shi and Benjun Guo and Yuanping Xu and Zhijie Xu and Jian Huang and Jun Lu and Dengguo Yao",
note = "Publisher Copyright: {\textcopyright} 2021 Chinese Automation and Computing Society in the UK-CACSUK.; 26th International Conference on Automation and Computing, ICAC 2021 ; Conference date: 02-09-2021 Through 04-09-2021",
year = "2021",
doi = "10.23919/ICAC50006.2021.9594189",
language = "English",
series = "2021 26th International Conference on Automation and Computing: System Intelligence through Automation and Computing, ICAC 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Chenguang Yang",
booktitle = "2021 26th International Conference on Automation and Computing",
}