ST-PCN: A Dynamic Point Cloud Classification Network Based on the Spatiotemporal Attention Mechanism of Millimeter-Wave Radar

With the advent of the intelligent era, human activity recognition has become increasingly important in application scenarios such as intelligent monitoring and human-computer interaction. The application of millimeter-wave radar in human behavior recognition has also emerged as a research hotspot in recent years. The point cloud data of millimeterwave radar can provide depth information in a three-dimensional space, which endows it with unique advantages in capturing spatial postures. This paper proposes a general method for human behavior recognition based on millimeter-wave radar point clouds. This method first processes the point cloud data of each frame in the spatial dimension to extract spatial features. Subsequently, it models the temporal dimension. By introducing attention mechanisms in both space and time, the model can focus on important features, thereby improving the accuracy of behavior recognition. Finally, the extracted features are classified through a multi-layer perceptron MLP . By comparing with other methods on public datasets, the results show that the proposed ST-PCN network model outperforms other baseline models, verifying its effectiveness and superiority