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摘要: 针对视觉遮挡引起的人体姿态估计(Human pose estimation, HPE)性能下降问题, 提出基于渐进高斯滤波(Progressive Gaussian filtering, PGF)融合的人体姿态估计方法. 首先, 设计分层性能评估方法对多视觉量测进行分类处理, 以适应视觉遮挡引起的量测不确定性问题. 其次, 构建分布式渐进贝叶斯滤波融合框架, 以及设计一种分层分类融合估计方法来提升复杂量测融合的鲁棒性和准确性. 特别地, 针对量测统计特性变化问题, 利用局部估计间的交互信息来引导渐进量测更新, 从而隐式地补偿量测不确定性. 最后, 仿真与实验结果表明, 相比于现有的方法, 所提的人体姿态估计方法具有更高的准确性和鲁棒性.Abstract: 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.
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图 6 不同滤波融合方法下的累积位置误差
Fig. 6 Cumulative position error under different filtering fusion methods
表 1 累积误差均值统计(mm)
Table 1 Cumulative error mean statistics (mm)
实验方法 腕关节 肘关节 肩关节 观测融合 166.44 124.44 96.56 CF 157.55 118.00 95.00 AMFKF 147.81 113.85 93.08 CI 127.63 117.85 99.62 IWCF 153.12 113.21 92.53 PGFFwoC 151.77 114.12 92.83 PGFFwC 119.47 108.98 84.11 
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