FMCW Multi-Person Action Recognition System Based on Point Cloud Nearest Neighbor Sampling Algorithm

This paper proposes a multi-person action recognition system based on frequency modulated continuous wave radar (FMCW). First, a Gaussian mixture model clustering method (GMM) is used to extract the active point cloud data of a single person from the data collected by the radar. Then point cloud nearest neighbor sampling algorithm is proposed for processing the radar point cloud data. Eventually, the long and short-term memory network (LSTM) is used to extract the features between time series data frames to realize action recognition. In this paper, a nearest neighbor sampling algorithm is proposed to solve the problem of sparse point cloud in single-person activity in the radar point cloud data processing. This method selects the reflection point closest to the centroid of the current frame based on the Euclidean distance, and merges multiple frames data with behavior information contained through a sliding window to fill in the information of the current frame and enrich the temporal and spatial sequence data. The radar point cloud data collected is then converted to fixed-dimensional feature information to meet the input requirements of the LSTM network. The experimental results show that the action recognition proposed in this paper has an average recognition accuracy of 98.68% for falling, sitting, and walking in a multi-person activity scene.