@article{M0EE92B7C,
title = "LSTM(Long Short-Term Memory)-Based AbnormalBehavior Recognition Using AlphaPose",
journal = "The Transactions of the Korea Information Processing Society",
year = "2021",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2021.10.5.187",
author = "Hyun-Jae Bae/Gyu-Jin Jang/Young-Hun Kim/Jin-Pyung Kim",
keywords = "Safety management, Action Recognition, Pose Estimation, LSTM, Deep Learning",
abstract = "A person's behavioral recognition is the recognition of what a person does according to joint movements. To this end, we utilize
computer vision tasks that are utilized in image processing. Human behavior recognition is a safety accident response service that combines
deep learning and CCTV, and can be applied within the safety management site. Existing studies are relatively lacking in behavioral
recognition studies through human joint keypoint extraction by utilizing deep learning. There were also problems that were difficult to
manage workers continuously and systematically at safety management sites. In this paper, to address these problems, we propose a method
to recognize risk behavior using only joint keypoints and joint motion information. AlphaPose, one of the pose estimation methods, was
used to extract joint keypoints in the body part. The extracted joint keypoints were sequentially entered into the Long Short-Term Memory
(LSTM) model to be learned with continuous data. After checking the behavioral recognition accuracy, it was confirmed that the accuracy
of the "Lying Down" behavioral recognition results was high."
}