Multi-view Human Pose Estimation Based on Progressive Gaussian Filtering Fusion

A human pose estimation (HPE) method based on progressive Gaussian filtering (PGF) fusion is proposed to address the performance degradation issue caused by visual occlusion. Firstly, a hierarchical performance evaluation method is designed to classify and handle multiple visual measurements, in order to adapt to the uncertainty problem caused by visual occlusion. Secondly, a distributed progressive Bayesian filtering fusion framework is constructed, and a hierarchical classification fusion estimation method is designed to improve the robustness and accuracy of complex measurement fusion. Specifically, to address the issue of measurement statistical characteristic variation, the interactive information among local estimators is utilized to guide the progressive measurement update, thereby implicitly compensating for measurement uncertainty. Finally, from simulation and experimental results, it demonstrates that compared with existing methods, the proposed human pose estimation method achieves higher accuracy and robustness. © 2024 Science Press. All rights reserved.