Realizing Human Pose Estimation Based on Deep Kalman Filtering

This paper presents a novel approach for pose estimation and tracking using millimeter-wave radar and deep Kalman filtering. The methodology put forth in this scholarly discourse adeptly utilizes the properties of angular velocity in tandem with the computational framework of Long Short-Term Memory (LSTM) networks. This approach synergistically amalgamates the prognostic capabilities inherent to angular velocity with the LSTM networks’ proficiency in discerning and encapsulating the temporal interdependencies present within the dataset pertaining to physical posture. Additionally, the paper introduces the application of transformer networks to compute inter-joint constraints and obtain accurate joint coordinates and velocities. Experimental evaluations validate the effectiveness of the proposed approach, demonstrating significant improvements in pose estimation precision and tracking accuracy compared to traditional methods.