A human pose estimation(HPE)method based on progressive Gaussian filtering(PGF)fusion is pro-posed 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 uncer-tainty 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 ac-curacy 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 up-date,thereby implicitly compensating for measurement uncertainty.Finally,from simulation and experimental res-ults,it demonstrates that compared with existing methods,the proposed human pose estimation method achieves higher accuracy and robustness.
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