基于随机森林误分类处理的3D人体姿态估计

为解决基于随机森林的3D人体姿态估计算法容易出现的误分类问题, 提出一种基于自适应融合特征提取和误分类处理机制的改进算法.该算法利用自适应融合特征提取方法自适应提取深度融合特征, 此特征可表达图像距离信息和部位尺寸信息, 增强特征的表征能力; 针对识别部位误分类问题, 分别从识别部位误分点聚集情况和迭代整合思想出发, 提出误分类处理机制, 改善部位识别结果; 最后提出可进一步处理误分点的改进主方向分析(Principal direction analysis, PDA)算法, 自适应计算出部位主方向向量, 实现3D人体姿态估计.结果表明, 该算法能有效去除部位误分点, 并显著改善了3D人体姿态估计.

3D Human Pose Estimation Based on Random Forest Misclassiflcation Processing Mechanism

This paper proposed an improved method which can reduce the misclassification in human pose estimation based on random forest and increase the accuracy, included adaptive fusion feature extraction and misclassification processing mechanism. Firstly, we improved the method of feature extraction to adaptive extract deep fusion feature with adaptive feature fusion extractive method, so that, both distance information and part information could enhance feature expression. Furthermore, owing to inspiration from error cluster analysis and iteration thought, the misclassification processing mechanism is proposed to handle misclassi-fication appearance. Finally, we achieved accurate human pose estimation from single depth images by applying the principal direction vector based on the improved principal direction analysis (PDA) algorithm. The experimental results demonstrated that this algorithm can efficiently eliminate several misclassifications and improve the accuracy of the 3D pose estimation.