Abstract: Human pose estimation,as a hot research topic in the field of action recognition,is widely applied in medical,security,and monitoring fields,and is of great significance for promoting the intelligent development of related industries.However,currently image-based human pose estimation has high environmental requirements and poor privacy.Based on this,a human pose estimation method based on millimeter wave radar point cloud is proposed.This method uses PointNet++ to extract features from millimeter wave radar point cloud.Compared with CNN based pose estimation methods,it has lower MSE,MAE,and RMSE values at each joint point.In addition,to solve the problem of sparse point clouds in millimeter wave radar,a multi frame point cloud stitching strategy is used to increase the number of point clouds.The model that concatenates three frame point clouds as input reduces the MSE and MAE values by 0.22 cm and 0.72 cm respectively compared to the original model,effectively alleviating the problem of excessively sparse point clouds.Finally,in order to fully utilize the temporal features between different point clouds,Transformer is combined with PointNet++,and the effectiveness of the multi frame point cloud stitching strategy and the addition of Transformer structure are demonstrated through ablation experiments.The MSE and MAE values reaches 0.59 cm and 5.41 cm respectively,providing a new approach for achieving better performance RF human pose estimation.

Key words: Human pose estimation, Millimeter wave radar, PointNet++, Point cloud data, Transformer