摘要
人体行为识别利用深度学习网络模型自动提取数据的深层特征,但传统机器学习算法存在依赖手工特征提取、模型泛化能力差等问题。提出基于空时特征融合的深度学习模型(CLT-net)用于人体行为识别。采用卷积神经网络(CNN)自动提取人体行为数据的深层次隐含特征,利用长短时记忆(LSTM)网络构建时间序列模型,学习人体行为特征在时间序列上的长期依赖关系。在此基础上,通过softmax分类器实现对不同人体行为分类。在DaLiAc数据集的实验结果表明,相比CNN、LSTM、BP模型,CLT-net模型对13种人体行为的总体识别率达到了97.6%,具有较优的人体行为识别分类性能。
Human activity recognition is a deep learning-based technology,which uses deep learning network models to automatically extract deep features of data.The traditional machine learning algorithms rely heavily on manual intervention during feature extraction,and exhibit a poor generalization ability.To address the problem,a deep learning model,CLT-net,is proposed based on space-time feature fusion for human activity recognition.CLT-net employs Convolution Neural Network(CNN)to extract the deep hidden features of human activity data automatically.Also,Long Short-Term Memory(LSTM)network is used to construct the time series model to learn the long-term dependence of human activity features on the time series.Finally,the softmax classifier is used to classify different human activities.The experimental results based on the public dataset,DaLiAc,show that CLT-net achieves an accuracy of 97.6%in the recognition of 13 kinds of human activities,outperforming the traditional models based on CNN,LSTM and BP.CLT-net has better classification performance of human activity recognition.
作者
孙彦玺
赵婉婉
武东辉
陈继斌
仇森
SUN Yanxi;ZHAO Wanwan;WU Donghui;CHEN Jibin;QIU Sen(College of Building Environment Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China;School of Control Science and Engineering,Dalian University of Technology,Dalian,Liaoning 116024,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2021年第10期260-268,共9页
Computer Engineering
基金
国家自然科学基金青年科学基金项目(61803072)
河南省科技攻关项目(182102210622)
河南省高等学校重点科研项目(19A413013)
郑州轻工业大学青年骨干项目(13501050002)
郑州轻工业大学博士科研项目(13501050009)。
关键词
人体行为识别
深度学习
卷积神经网络
长短时记忆网络
模式识别
可穿戴传感器
human activity recognition
Deep Learning(DL)
Convolutional Neural Network(CNN)
Long Short-Term Memory(LSTM)network
pattern recognition
wearable sensors
