基于姿态估计的实时跌倒检测算法
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作者单位:

(1. 北京工业大学信息学部,北京100124;2. 计算智能与智能系统北京重点实验室,北京100124;3. 数字社区教育部工程研究中心,北京100124)

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E-mail: yunaigong@bjut.edu.cn.

中图分类号:

TP391

基金项目:

国家自然科学基金项目(61573029);国家重点研发计划项目(2017YFC0209905).


Real-time fall detection algorithm based on pose estimation
Author:
Affiliation:

(1. Faculty of Information Technology,Beijing University of Technology,Beijing100124,China;2. Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing100124,China;3. Digital Community Ministry of Education Engineering Research Center,Beijing100124,China)

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    摘要:

    为了快速、准确地检测老年人跌倒事件的发生,给出一种基于姿态估计的实时跌倒检测算法.首先利用基于深度学习的人体姿态估计算法得到人体关节点的坐标;然后,通过计算人体质心点的下降速度、跌倒后颈部关节点的纵坐标值是否大于阈值、以及肩部和腰部关节点的相对位置关系来判断跌倒是否发生.所给出的跌倒检测算法利用单目相机进行检测,便于以嵌入式方式应用于机器人.实验结果表明,所提出算法与当前先进方法相比取得了较好的效果.

    Abstract:

    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.

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引用本文

于乃功,柏德国.基于姿态估计的实时跌倒检测算法[J].控制与决策,2020,35(11):2761-2766

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  • 在线发布日期: 2020-10-15
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