Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches
Author:
Affiliation:
1. Department of Software, Sejong University, Seoul, South Korea
2. Department of Computer Science and Engineering, Sejong University, Seoul, South Korea
3. Department of Computer Education, Sungkyunkwan University, Seoul, South Korea
Funder
the ITRC (Information Technology Research Center) support program
the Technology Development Program through the Korean Ministry of Small and Medium Enterprises
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/10005208/10225507.pdf?arnumber=10225507
Reference192 articles.
1. KAMTFENet: a fall detection algorithm based on keypoint attention module and temporal feature extraction
2. Exploring Human Pose Estimation and the Usage of Synthetic Data for Elderly Fall Detection in Real-World Surveillance
3. A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
4. Robust Pose-Based Human Fall Detection Using Recurrent Neural Network
5. Theory of the Backpropagation Neural Network**Based on “nonindent” by Robert Hecht-Nielsen, which appeared in Proceedings of the International Joint Conference on Neural Networks 1, 593–611, June 1989. © 1989 IEEE.
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