摘要
障碍行为指有特殊需要人群的伤害性行为,对其进行分类和识别是人类行为识别的一个重要分支。针对多传感器设备布设于人体不同部位来感知障碍行为的过程中存在传感器之间相关性未能被考量的问题,基于深度学习理论,提出了利用多头-连体神经网络表征传感器之间相关性的方法。该网络在权值共享的基础上,构建多个子网络对多传感器数据分别进行一致的特征提取操作,并对所提取的特征进行融合后通过分类器进行识别。首先对于传感器中存在的缺失数据进行上采样并进行数据标准化分析,其次以贝叶斯优化对该网络的超参数进行选取。然后,由于引入Adam优化器在障碍行为识别领域存在着过拟合的问题,改用AdamW优化器进行L2正则化处理并能够提升网络的识别准确率。实验结果表明:此网络对障碍行为的分类准确率约达到96.0%,相对于基线网络和单头网络,分类准确率分别提高了约6.1%和8.8%并降低了部分行为之间存在的误识问题。相对于多头网络分类准确率提高了约2.4%,并减少了约92.22%的训练参数量。通过解决传感器之间的关联性问题,证明此网络对于障碍行为的识别具有有效性。
Impaired behavior recognition is an important branch of human activity recognition,which refers to harmful behavior of people with special needs.Aiming at the problem that the correlation between sensors is not taken into account when recognizing the impaired behavior by using multi-sensor devices equipped on different parts of the human body,based on deep learning theory,this paper proposes a multi-head-siamese neural network to characterize the relation between sensors,which builds multiple sub-networks for consistent feature extraction.The extracted features are fused and recognized by the classifier on the basis of the weight sharing idea.In the presented network,the upsampling operation is first employed to fill the missing collected data,and the data is then standardized to improve the recognition accuracy.Besides,the network hyperparameters are adjusted by the Bayesian optimization.In addition,due to the over-fitting problem when recognizing impaired behavior by introducing the Adam optimizer,L2 regularization is performed by using the AdamW optimizer,thus further improving the recognition accuracy.Processing results of raw data show that the network achieves a classification accuracy of 96.0%.Compared with the baseline network and single input network,the accuracy of the proposed network increases by 6.1%and 8.8%,respectively,and it could reduce the possibility of incorrect prediction.Compared with the multiple input network,its accuracy increases by 2.4%,and it reduces the number of training parameters by 92%.It is proved that this network is effective for impaired behavior recognition in terms of utilizing the relationship between sensors.
作者
马仑
刘鑫
赵斌
王瑞平
廖桂生
张亚静
MA Lun;LIU Xin;ZHAO Bin;WANG Ruiping;LIAO Guisheng;ZHANG Yajing(School of Information Engineering,Chang’an University,Xi’an 710064,China;National Lab of Radar Signal Processing,Xidian University,Xi’an 710071,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2022年第4期100-108,175,共10页
Journal of Xidian University
基金
中国博士后科学基金(2015M582586)
长安大学大学生创新创业训练计划(S202110710222)。
关键词
行为识别
深度学习
神经网络
权值共享
特征提取
behavior recognition
deep learning
neural network
weight sharing
feature extraction
