基于时空注意的毫米波雷达人体活动识别网络

为了解决传统方式利用摄像头进行人体活动识别抗干扰性差以及侵犯用户隐私的问题，提出一种基于时空注意的毫米波雷达3D点云数据的人体活动识别网络，以实现智能应用上下文的准确感知。该网络首先使用二级滑动时间窗口分别累积和分离人体活动产生的点云数据作为分类器的输入，利用PointLSTM单元根据点云坐标关系聚合点特征和状态以提取人体活动的时间序列特征；然后拼接时空特征，通过采样分组模块降低整体网络计算量以及提升网络对局部特征的聚合能力；最后使用堆叠的注意力模块深度融合动态点云数据时空上的全局和局部特征以完成对人体活动任务的准确分类。利用毫米波雷达采集了多种人体活动点云数据集，实验结果表明，提出的时空注意网络平均准确度可达98.64%，能够有效识别复杂且差异小的人体活动类型，完成人体活动识别系统的要求。

Human activity recognition network for millimeter-wave radar based on spatio-temporal attention

In order to solve the problems of poor anti-interference and invasion of user privacy in traditional methods of using cameras for human activity recognition, this paper proposed a human activity recognition network based on spatio-temporal attention of millimeter wave radar 3D point cloud data to achieve accurate perception of intelligent application context. The network firstly used a secondary sliding time window to accumulate and separate the point cloud data generated by human activities as the input of the classifier, then used the PointLSTM unit to aggregate point features and states according to the point cloud coordinate relationship to extract the time sequence features of human activities, and then spliced temporal-spatial features, reduced the overall network computation and enhanced the network''s aggregation ability for local featured through sampling grouping modules, and finally used a stacked attention module to deeply fuse global and local features in temporal-spatial point cloud data to complete the accurate classification of human activities. This paper used millimeter wave radar to collect point cloud datasets of various human activities, the experimental results show that the average accuracy of the proposed spatiotemporal attention network can reach 98.64%, which can effectively identify complex and small-difference human activities, and meet the requirements of the human activity recognition system.