Real-time fall detection algorithm based on pose estimation

被引：0
|
作者：
Yu N.-G. [1 ,2 ,3 ]
Bai D.-G. [1 ,2 ,3 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
[2] Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing
[3] Digital Community Ministry of Education Engineering Research Center, Beijing
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 11期
关键词：
Deep learning; Embedding; Fall detection in real-time; Human pose estimation algorithm; Monocular camera; Robots;
D O I：
10.13195/j.kzyjc.2019.0382
中图分类号：
学科分类号：
摘要：
In order to quickly and accurately detect the occurrence of falls in the elderly, this paper presents a real-time fall detection algorithm based on pose estimation.Firstly, the human pose estimation algorithm based on deep learning is used to obtain the coordinates of the joint point. Then, by calculating the falling speed of the centroid point when the human body falls, whether the ordinate value of the neck joint point after the fall is greater than the threshold, and the relative positional relationship of the shoulder-waist joint point in the image, whether the fall occurs is determined.The algorithm uses a monocular camera to detect, which is easily used in an embedded way for robots.The experimental results show that compared with the current advanced methods, the proposed algorithm has achieved good results. © 2020, Editorial Office of Control and Decision. All right reserved.
引用
收藏
页码：2761 / 2766
页数：5
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