电子测量与仪器学报

司海飞,胡兴柳,史震.基于背景减除和特征提取的跌倒识别方法[J].电子测量与仪器学报,2020,34(10):201-207

基于背景减除和特征提取的跌倒识别方法

Fall recognition method based on background subtraction and feature extraction

DOI：

英文关键词:behavior recognition feature extraction center of gravity detection algorithm

基金项目:江苏省自然科学基金面上项目(BK20171114)、国家自然科学基金(61873002)、江苏高校“青蓝工程”中青年学术带头人、金陵科技学院人才引进项目(Jit-rcyj-201604)资助

作者	单位
司海飞	1. 金陵科技学院智能科学与控制工程学院，2. 哈尔滨工程大学智能科学与工程学院
胡兴柳	1. 金陵科技学院智能科学与控制工程学院
史震	2. 哈尔滨工程大学智能科学与工程学院

Author	Institution
Si Haifei	1. College of Intelligent Science and Control Engineering, Jinling Institute of Technology，2. College of Intelligent Systems Science and Engineering, Harbin Engineering University
Hu Xingliu	1. College of Intelligent Science and Control Engineering, Jinling Institute of Technology
Shi Zhen	2. College of Intelligent Systems Science and Engineering, Harbin Engineering University

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中文摘要:

为了有效监测老年人是否跌倒,提出一种结合背景减除及人体边界的外部轮廓特征提取方法对人体动作行为进行识别。首先,利用背景减除法从视频中提取运动的对象,对提取的运动对象进行预处理;然后利用最小外接矩形和重心检测的方法对运动目标进行特征提取,得到老人整体外部轮廓和重心位置等运动特征;最后根据人体不同姿态,建立运动模型,有效辨识被监护对象的行走、跌倒等动作。实验结果表明,提出的方法可对实际的视频进行有效处理,对人体行为识别的准确性能达到 94. 3%。

英文摘要:

In order to effectively monitor whether the elderly fall, an external contour features extraction combining background subtraction based on human body boundary was proposed, which identify human movement behavior. Firstly, the background subtraction method was used to extract the moving objects from the video, and then the pictures of extracted moving objects were preprocessed. Secondly, the detection method of the minimum external rectangle and the center of gravity was used to extract the moving objects’ features, so as to obtain the overall external contour and the center of gravity position of the elderly. Finally, according to the different positions of human body, the movement model was established to effectively identify the movement of the monitored object, such as walking, falling, etc. The experimental results show that the algorithm proposed in this paper can effectively process the actual video, and the accuracy of human behavior recognition reaches 94. 3%.

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