基于Kinect传感器的跌倒行为的检测与分析  

Fall Behavior Detection and Analysis Using a Kinect Sensor

作　　者：马宗方[1] 李静[1,2] 曹陇鑫 Zongfang Ma;Jing Li;Longxin Cao(School of Information and Control Engineering,Xi'an University of Architecture and Technology,Xi'an,Shanxi 710055,China;Baowu Equipment Intelligent Technology Co.,Ltd.Shanghai,201900,China)

机构地区：[1]西安建筑科技大学信息与控制工程学院,陕西西安710055 [2]宝武装备智能科技有限公司,上海201900

出　　处：《激光与光电子学进展》2020年第21期80-84,共5页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61701388);陕西省教育厅产业化项目(18JC017);陕西省自然科学基础研究计划(2018JM6080);西安建筑科技大学青年科技基金(QN1630)。

摘　　要：高危作业现场环境复杂,危险系数高,容易发生跌倒事故,造成人员伤亡。为了检测工人跌倒行为,提出了一种基于Kinect传感器的人体跌倒检测方法。利用Kinect获取深度图像,提取关节点信息,通过计算关节点相对位置熵和速度的变化,判断人体是否发生跌倒。通过对比实验,确定了一组跌倒识别率最高的骨架关节点:头、双肩、双膝、中心点。实验数据表明该方法可以更快速准确地检测出跌倒行为。High-risk work site environments are complex and dangerous and responsible for many fall accidents and casualties.To detect the fall behavior of workers,a human fall detection method using a Kinect sensor was proposed.Based on depth images obtained using a Kinect,we extracted body joint points information and determined whether a human body fell by calculating the changes of the relative position entropy and speed of the joint points.Through comparative experiments,a set of skeleton joint points with the highest fall recognition rate were determined:head,shoulders,knees,and center points.Experimental data show that the method can detect fall behaviors more quickly and accurately compared with the conventional methods.

关 键 词：探测器 KINECT 信息熵 关键点 阈值分析 实时检测 

分 类 号：TN919.81[电子电信—通信与信息系统]