计算机应用与软件2025，Vol.42Issue(5)：155-163,9.DOI:10.3969/j.issn.1000-386x.2025.05.022
基于改进自适应卡尔曼滤波的遮挡场景人体关节重定位方法研究
HUMAN JOINT ESTIMATION METHOD BASED ON IMPROVED ADAPTIVE KALMAN FILTER FOR OBSCURED SCENES
摘要
Abstract
To address the problem of jitter and missing human joint data due to Kinect V2's own error and joint occlusion,a method is proposed to integrate human kinematic features with an improved adaptive Kalman filtering algorithm.We introduced the filter convergence criterion and skeletal distortion coefficient into the adaptive Kalman filtering algorithm to reduce the computational effort of the algorithm and accelerate the convergence of the adaptive parameters,and combined the human skeletal length invariance and motion continuity to obtain the a priori coordinate measurements of the occluded joints,and then substituted the improved adaptive Kalman filtering algorithm to obtain the relocation coordinates of the occluded joints.The experimental results show that the method can meet the user's real-time requirements and effectively improve the accuracy of human joint data.关键词
Kinect V2/骨骼数据/自适应卡尔曼滤波/人体运动学Key words
Kinect V2/Skeletal data/Adaptive Kalman filter/Human kinesiology分类
计算机与自动化引用本文复制引用
李国友,卢凯,李宏,张友浪,柴子华..基于改进自适应卡尔曼滤波的遮挡场景人体关节重定位方法研究[J].计算机应用与软件,2025,42(5):155-163,9.基金项目
河北省自然科学基金项目(F2012203111) （F2012203111）
河北省高等学校自然科学研究青年基金项目(2011139). （2011139）
