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
叶永雪 1马鸿雁2
作者信息
- 1. 北京建筑大学电气与信息工程学院 北京 100044
- 2. 北京建筑大学电气与信息工程学院 北京 100044;建筑大数据智能处理方法研究北京市重点实验室 北京 100044;智慧城市国家级虚拟仿真实验教学中心 北京 100044
- 折叠
摘要
针对跌倒检测中存在由于障碍物而无法检测的问题,提出基于可移动机器人的跌倒检测模型.基于机器人姿态控制技术,在机器人关节约束的条件下,利用双上位机采集NAO机器人姿态数据集.基于融合的方向直方图与灰度共生矩阵双特征,建立双特征跌倒检测模型.基于ROS可移动机器人,实现跌倒检测模型对NAO机器人在不同场景的跌倒检测.实验结果表明,在大数据基础下,双特征融合的跌倒检测较单一特征的跌倒检测提高了摔倒检测的准确性.该算法研究适用于实际工程,适用于老年人跌倒检测.
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引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据