A Novel Multiperson Activity Recognition Algorithm Based on Point Clouds Measured by Millimeter-Wave MIMO Radar

Radar is widely used in human activity recognition (HAR). The radar-based HAR algorithms can be roughly categorized into feature map-based and point cloud-based. Between the two, the point cloud-based algorithms are more suitable for multiperson activity recognition (MPAR) tasks due to the spatial properties of point clouds and are therefore increasingly attracting attention. However, most existing point cloud-based algorithms make decisions by utilizing either Doppler or coordinate features of point clouds, which are not sufficient to characterize the activity; the remaining algorithms have large randomness in extracting both features. Neither of these algorithms can fully exploit the advantage of feature fusion, which is the key to recognition performance. To address this shortcoming, this article proposes a novel MPAR algorithm. First, a feature mapping approach is proposed and defined in equation form, the Doppler, range, azimuth, and elevation features of the point clouds are calculated and accumulated sequentially, so we can obtain the four time-domain feature maps. Second, with the four feature maps as inputs, a four-channel convolutional neural network (CNN) classification model with channel attention is trained for MPAR tasks. Datasets of multi-person activities are collected respectively under indoor circumstances and aquatic circumstances using the millimeter-wave multiple-input–multiple-output (MIMO) radar platform. The dataset-based evaluation performance shows that the proposed algorithm achieves accuracy results of 96.09% for the indoor-MPAR task and 93.97% for the aquatic-MPAR task, and outperforms three conventional point cloud-based algorithms in terms of the overall MPAR accuracy, the generalization ability to the aquatic activity recognition, and the robustness of distance and headcount.