基于特征融合的毫米波雷达行为识别算法

Millimeter Wave Radar Human Activity Recognition Algorithm Based on Feature Fusion

基于毫米波雷达的人体行为识别方法以远程非接触的方式捕获人类活动的电磁波信号并进行识别,不受烟雾和光线等的干扰,具有一定的隐私保护性,是当前的一个研究热点。针对现有的算法存在特征输入单一、模型结构复杂、泛化能力验证性不够等问题,提出了基于双分支特征融合卷积神经网络(Two Steam Features Fusion Convolutional Neural Network,2S-FCNN),使用搭载注意力机制的残差神经网络作为骨干网络,并行输入时间距离图和时间速度图,采用特征加权分数融合的方式融合特征后进行分类识别,实现了较高的识别准确率。在公开数据集和自建数据集上与现有的其他算法进行了深入的对比实验,实验结果表明所提算法在识别率和泛化能力方面都具有良好的性能。

The human activity recognition method based on millimeter-wave radar captures the electromagnetic wave signals of human activities in non-contact way for recognition.It is not easily interfered by smoke and light,which has a certain degree of privacy protection,and has become a research hotspot at present.However,the existing algorithms have some problems,such as single feature input,complex model structure,and insufficient generalization ability verification.A human activity recognition algorithm with two stream feature fusion convolutional neural network is proposed,named 2S-FCNN,which uses the residual neural network equipped with attention mechanism as the backbone network,inputs the time-distance image and the time-velocity image in parallel,and uses the feature weighted score fusion method to fuse the features for classification and recognition,so as to achieve a high recognition accuracy.A set of in-depth comparative experiments are conducted with other existing algorithms on both public and self built datasets,and the experimental results show that the proposed algorithm has good performance in recognition rate and generalization ability.