基于微多普勒角点特征与Non-Local机制的穿墙雷达人体步态异常终止行为辨识技术

Human Anomalous Gait Termination Recognition via Through-the-wall Radar Based on Micro-Doppler Corner Features and Non-Local Mechanism

穿墙雷达能够穿透建筑物墙体,实现室内人体目标探测。利用深度学习提取不同肢节点的微多普勒特征,可以有效辨识障碍物后的人体行为。但是,当生成训练、验证集与生成测试集的受试者不同时,基于深度学习的行为识别方法测试准确率相对验证准确率往往较低,泛化能力较差。因此,该文提出一种基于微多普勒角点特征与Non-Local机制的穿墙雷达人体步态异常终止行为辨识技术。该方法利用Harris与Moravec检测器提取雷达图像上的角点特征,建立角点特征数据集;利用多链路并行卷积和Non-Local机制构建全局上下文信息提取网络,学习图像像素的全局分布特征;将全局上下文信息提取网络重复堆叠4次得到角点语义特征图,经多层感知机输出行为预测概率。仿真和实测结果表明,所提方法可以有效识别室内人体步行过程中存在的坐卧、跌倒等突发步态异常终止行为,在提升识别准确率、鲁棒性的前提下,有效控制泛化精度误差不超过6.4%。

Through-the-wall radar can penetrate walls and realize indoor human target detection.Deep learning is commonly used to extract the micro-Doppler signature of a target,which can be used to effectively identify human activities behind obstacles.However,the test accuracy of the deep-learning-based recognition methods is low with poor generalization ability when different testers are invited to generate the training set and test set.Therefore,this study proposes a method for recognition of anomalous human gait termination based on micro-Doppler corner features and Non-Local mechanism.In this method,Harris and Moravec detectors are utilized to extract the corner features of the radar image,and the corner feature dataset is established in this manner.Thereafter,multilink parallel convolutions and the Non-Local mechanism are utilized to construct the global contextual information extraction network to learn the global distribution characteristics of the image pixels.The semantic feature maps are generated by repeating four times the global contextual information extraction network.Finally,the probabilities of human activities are predicted using a multilayer perceptron.The numerical simulation and experimental results demonstrate that the proposed method can effectively identify such abnormal gait termination activities as sitting,lying down,and falling,among others,which occur in the process of indoor human walking,and successfully control the generalization accuracy error to be no more than 6.4%under the premise of increasing the recognition accuracy and robustness.