计算机应用与软件2024，Vol.41Issue(12)：69-76,8.DOI:10.3969/j.issn.1000-386x.2024.12.011
基于特征融合的机器人视觉跌倒检测研究
ROBOT VISION FALL DETECTION BASED ON FEATURE FUSION
摘要
Abstract
Aimed at the problem that fall detection can not be detected due to obstacles,a fall detection model based on mobile robot is proposed.Based on the robot attitude control technology,under the constraint of robot joint,the data set of NAO robot attitude was collected by using double upper computers.Based on the fusion of direction histogram and gray level co-occurrence matrix,a dual feature fall detection model was established.Based on ROS mobile robot,the fall detection model of NAO robot in different scenes was realized.The experimental results show that,based on big data,the fall detection of dual feature fusion improves the accuracy of fall detection compared with the fall detection of single feature.This algorithm is suitable for practical engineering and fall detection of the elderly.关键词
数据采集/跌倒检测/多特征融合/机器人控制/支持向量机/语音交互Key words
Data acquisition/Fall detection/Feature fusion/Robot control/Support vector machine/Voice interaction分类
信息技术与安全科学引用本文复制引用
叶永雪,马鸿雁..基于特征融合的机器人视觉跌倒检测研究[J].计算机应用与软件,2024,41(12):69-76,8.基金项目
北京建筑大学博士基金项目(ZF15054). （ZF15054）
