###
计算机系统应用英文版:2020,29(7):186-192
本文二维码信息
码上扫一扫!
基于双摄像头的摔倒检测技术
(江南大学 物联网工程学院, 无锡 214122)
Fall Detection Technology Based on Dual Cameras
(School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 1132次   下载 1713
Received:December 19, 2019    Revised:January 03, 2020
中文摘要: 随着人口老龄化趋势的加快, 老人独居现象增多, 为了减少老人摔倒所带来的伤害, 本文对基于双摄像头的摔倒检测技术进行研究. 针对Vibe算法在运动目标检测过程中存在的鬼影问题, 结合了帧间差分法进行鬼影区域的判断, 加快了鬼影的消除, 避免了其干扰. 利用人体外接矩形对检测到的人体进行标记, 求取出人体运动过程中高度、外接矩形高宽比、质心、Hu矩特征, 通过基于阈值分析法和支持向量机(SVM)的摔倒检测算法判断是否摔倒. 为了提高摔倒行为的检测率, 提出采用双摄像头进行联合判断. 实验结果表明, 系统能有效识别摔倒与其他日常行为, 算法准确度高、实时性好.
Abstract:With the aging population and more and more people living alone, a fall detection system based on dual cameras is proposed to reduce the damage caused by falls. Aiming at the ghost problem of Vibe algorithm in the process of moving target detection, this study combines the frame difference method to judge ghost area, which speeds up ghost elimination and avoids its interference. The minimum outer rectangle of the human body is used to mark the detected human body, and the height, aspect ratio, centroid, and Hu moment characteristics of the outer rectangle are obtained. The fall detection algorithm based on threshold analysis and Support Vector Machine (SVM) is used to judge whether or not the human body falls. In order to improve the detection rate of fall behavior, dual cameras are proposed for joint judgment. The experimental results show that the system can effectively distinguish fall from other daily behaviors, and the algorithm has high accuracy and real-time performance.
文章编号:     中图分类号:    文献标志码:
基金项目:
引用文本:
张飞,朱建鸿.基于双摄像头的摔倒检测技术.计算机系统应用,2020,29(7):186-192
ZHANG Fei,ZHU Jian-Hong.Fall Detection Technology Based on Dual Cameras.COMPUTER SYSTEMS APPLICATIONS,2020,29(7):186-192