# Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
## 1 Introduction
In this paper, the data preprocessing module is designed to superimpose some traditional filtering methods, and the filtering method proposed in this paper is based on the inter-frame differences of millimeter-wave point clouds to filter and reduce the noise of the original point cloud. Secondly, multiplayer action recognition must segment the multiplayer point cloud. This paper uses Density-Based Spatial Clustering of Applications with the noise algorithm [17] and the Hungarian algorithm [18] to cluster the point clouds belonging to the same target in each frame. At the same time, the data of the same target in consecutive multi-frames are connected and matched [19] to complete the segmentation of the multi-person point cloud. Then, due to the sparsity of the millimeter-wave point cloud, inputting each frame's data into the deep learning model will generate a substantial computational overhead. Therefore, in this paper, we design a data optimization dimension reduction module and superimpose six frames of data so that it not only helps to overcome the sparsity and uncertainty of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Equipment Type** & **Resolution** & **Multiplayer** & \
\begin{tabular}{c} **Privacy** \\ **Protection** \\ \end{tabular} &
\begin{tabular}{c} **Affected by** \\ **Weather** \\ \end{tabular} \\ \hline camera equipment & highest & yes & no & yes \\ wearable equipment & high & no & yes & no \\ infrared equipment & low & yes & yes & no \\ millimeter-wave radar & high & yes & yes & no \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different human action recognition devices. the millimeter-wave point cloud, but also can significantly reduce the computational cost. Since most human actions can be accurately described in the XZ dimensions, we discard the \(Y\)-axis component. While reducing the dimensionality of the data, the critical information in the data is retained as much as possible, creating certain conditions for the subsequent use of the CNN + LSTM deep learning model [20]. Finally, the CNN + LSTM deep learning model is used in this paper. Human action is a process highly correlated with temporal changes, and this approach not only extracts the spatial features of human action, but also fully considers the temporal features. Our proposed method achieves 92.2% recognition accuracy in multi-person scenarios. This paper makes the following contributions:  
1. We propose an integrated framework for multi-person action recognition based on millimeter-wave radar point clouds. 2. Based on the characteristics of the millimeter-wave radar point clouds, we introduce a filtering method based on the inter-frame differences of the millimeter-wave point clouds. 3. Using the collected multi-millimeter-wave radar human point cloud data, we achieved an action classification accuracy of 92.2% in multi-person scenarios. The sections of this paper are organized as follows: Section 2 describes the system design in detail. In Section 3, our experimental setup and data acquisition are described. Section 4 discusses the robustness and ablation experiments and classification results. In Section 5, we share the conclusions.