中文版 | English
题名

基于毫米波雷达与深度学习的行为识别研究

其他题名
RESEARCH ON BEHAVIOR RECOGNITION BASED ON MILLIMETER WAVE RADAR AND DEEP LEARNING
姓名
姓名拼音
SHEN Ying
学号
12132139
学位类型
硕士
学位专业
0856 材料与化工
学科门类/专业学位类别
0856 材料与化工
导师
蹇林旎
导师单位
电子与电气工程系
论文答辩日期
2023-05-18
论文提交日期
2023-06-29
学位授予单位
南方科技大学
学位授予地点
深圳
摘要
  随着人们年龄的增长以及生活速度的加快,跌倒事故正日益成为一个社会上的严峻问题,因此需要我们对日常行为进行识别与分析以更好监测跌倒行为。目前主要的监测设备仅关注摔倒动作的发生,而未能综合分析各类动作信息进行判断,且存在侵犯用户隐私与使用不便的问题。综合考虑用户体验感与监测的高效与灵活性,本文选择了非穿戴式的连续调频毫米波雷达作为行为识别与跌倒动作监测的设备,其能获取目标的距离和位置等信息,更利于跌倒检测。综合考虑,本文在基于毫米波雷达与深度学习的基础上,对动作识别与跌倒监测技术进行研究。
  本文主要工作如下:
 (1)采用毫米波雷达设备在多类真实室内场景下采集 51 名实验对象的行走、坐下、躺下、摔倒四种不同的瞬时动作数据,搭建四分类动作的多场景数据集。
 (2)根据雷达原始数据信息,数据预处理部分结合 DBSCAN 聚类与卡尔曼
滤波算法进行点云聚类、去噪与目标追踪。由提取到的数据设计了包含目标高度的特征与目标轨迹信息的 RGB 图像特征,根据不同特征选择对应分类器,通过对准确率等性能进行比较,选择最优版本的姿态特征数据与分类器。
 (3)基于最优特征与分类器,为提高分类系统性本文在动作识别后再结合阈值判断对行为加以验证。结合阈值判断后减少了站立等其余动作的误判率,最终新场景下的动作识别测试准确率可达 95%
关键词
语种
中文
培养类别
独立培养
入学年份
2021
学位授予年份
2023-06
参考文献列表

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所在学位评定分委会
材料科学与工程
国内图书分类号
TN958.98
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人工提交
成果类型学位论文
条目标识符http://sustech.caswiz.com/handle/2SGJ60CL/544673
专题工学院_电子与电气工程系
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沈滢. 基于毫米波雷达与深度学习的行为识别研究[D]. 深圳. 南方科技大学,2023.
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