Research On Human Posture Estimation Algorithm Based On RF Signal

Human posture estimation is a major research topic in the field of motion recognition.Its main research content is to recognize and extract human character images,so as to describe the motion posture of human body.Human pose estimation is mainly used in various scenes of human motion prediction and location.In the past,this task was often solved by the traditional graph structure method,but its recognition accuracy and performance were poor due to the high requirements of image background and large time overhead.In recent years,researchers of human pose estimation are no longer limited to traditional methods,but rely on neural network to learn the characteristics of video data,so as to improve the recognition rate of the algorithm.However,video data has some problems in some application scenarios,such as difficult data acquisition or insufficient feature information.Therefore,more and more researchers turn to RF attitude estimation to break through the application limitations brought by data types.In this paper,the problem of RF attitude estimation is studied,and the following work is done.(1)At present,the available RF raw data are often affected by physical blocking and other factors,and there are a large number of interference signals that need to be filtered.At the same time,the existing algorithms still have great room for improvement in recognition rate and timeliness.Aiming at the shortcomings of RF field,this paper relies on millimeter wave acquisition equipment for raw data acquisition and processing,and puts forward a more efficient key point prediction algorithm.The algorithm includes two steps of human key point location and combination,and combines the stacked hourglass network and residual network to form a multi-task model architecture.After training the algorithm network,it can effectively improve the feature learning rate of the traditional network and complete the task of pose estimation.The simulation results on millimeter wave radar data set show that the algorithm can effectively improve the detection accuracy of attitude estimation,and further prove the feasibility of RF attitude estimation.(2)With the increase of the number of attitude estimation detection personnel,the existing algorithms often affect the detection task due to the limb interference between personnel,resulting in a significant decline in the accuracy.In the multi-person scene,the problem of wrong connection of key points caused by human adjacent,collision or occlusion is more obvious,and greatly improves the difficulty of solving the key point combination problem.Based on the above problems,this paper optimizes the multi-person heat map algorithm and proposes a multi-person key point detection algorithm based on partial correlation domain.In order to improve the detection effect in multi-person scenario,the algorithm introduces some associated domain tags,and constructs the sub network after the main network.The simulation results show that the multi-task network algorithm based on partial correlation domain information has certain advantages in detection accuracy and model efficiency.