摘要
基于人体骨骼数据分析人体行为的方法可解释性强,在基于视觉的人体行为分析研究中具有明显优势。但视角干扰及目标遮挡严重影响人体骨骼关节点的标定。本文提出了一种在人体结构约束条件下的基于人体姿态特征的人体骨骼关节点估计算法,并根据骨骼数据识别人体行为。首先根据人体运动的稳态趋势和暂态变化,基于决策树和加权线性回归分别建立特征提取模型,对缺失或混淆的关节点进行估计。然后设计了一个结合轻量级时间卷积和注意力图卷积的行为识别网络模型,针对行为样本的时间尺度优化模型。在NTU RGB+D 60数据集中建立遮挡情况进行实验,准确率分别达到90.28%(CV)与81.95%(CS),且在UTD-MHAD数据集中达到98.2%,均优于现有方法。
The method of analyzing human behavior based on human skeleton data is highly interpretable and has obvious advantages in the research of human behavior analysis based on vision.However,viewing angle interference and target occlusion seriously affect the calibration of human skeleton joints.This paper proposes a human skeleton joint point estimation algorithm based on human pose features under the constraints of human structure,and recognizes human behavior based on skeleton data.Firstly,according to the steady-state trend and transient changes of human motion,feature extraction models are established based on decision tree and weighted linear regression,respectively,to estimate missing or confused joint points.Then,an action recognition network model combining lightweight temporal convolution and attention graph convolution is designed to optimize the model for the time scale of action samples.The occlusion condition was established in the NTU RGB+D 60 dataset for experiments,and the accuracy rates were 90.28%(CV)and 81.95%(CS),respectively,and 98.2%in the UTD-MHAD dataset,which were better than those of the existing methods.
作者
李志晗
刘银华
谢锐康
单良
Li Zhihan;Liu Yinhua;Xie Ruikang;Shan Liang(Automaion Institute,Qingdao University,Qingdao 266071,China;Institute for Future,Qingdao University,Qingdao 266071,China)
出处
《电子测量技术》
北大核心
2022年第24期153-160,共8页
Electronic Measurement Technology
基金
国家重点研发计划重点专项(2020YFB1313600)资助
关键词
行为识别
人体姿态估计
时间卷积
图卷积
运动估计
action recognition
human pose estimation
temporal convolution
graph convolution
motion estimation
作者简介
李志晗,硕士研究生,主要研究方向为图像处理,行为识别。E-mail:2020020580@qdu.edu.cn;刘银华,博士,副教授,主要研究方向为计算机视觉。E-mail:liuyinhua@qdu.edu.cn;谢锐康,本科,主要研究方向为计算机领域。E-mail:2019204051@qdu.edu.cn;单良,本科,主要研究方向为计算机领域。E-mail:2019204046@qdu.edu.cn
