基于深度图像的手部姿态估计综述

A Survey on Depth Based Hand Pose Estimation

基于深度图像的手部姿态估计是人机交互和虚拟现实领域的一个重要研究问题.对近些年来该领域的研究工作进行总结和梳理.首先,简述了该问题的定义以及所面临的主要难点,并总结了常用的深度相机、数据集和评价指标;其次,将该领域内的工作分为3个类别并依次进行回顾,其中包括基于模型驱动的方法、从数据集中学习映射函数的基于数据驱动的方法以及同时结合了前两者的混合方法,在叙述过程中,着重介绍了其解决的科学问题以及仍存在的缺陷;最后,从算法的准确性、适用性和鲁棒性3个角度对这些工作分别进行进一步的分析,并对未来的研究方向进行展望.

Depth-based hand pose estimation has received increasing attention in the fields of human-computer interaction and virtual reality.A comprehensive survey and analysis of depth-based hand pose estimation of recent works are conducted.First,the definition and difficulties of this problem are explained,the widely used sensor and public datasets are also introduced.Then,the works of this field are divided into three categories,model-driven,data-driven,and hybrid method.The model-driven methods perform a model fitting between the model and the depth points.The data-driven methods learn a function,which maps the depth image to pose.The hybrid methods combine model-driven and data-driven to recovery the hand pose.In the course of narra-tion,we focus on the solved problems and shortcomings to be solved.In the final,the works are compared in terms of accuracy,suitability,and robustness.The future research in this direction is also discussed.