Real-time and robust hand tracking with a single depth camera
Real-time and robust hand tracking with a single depth camera
复制标题
DOI：
10.1007/s00371-013-0894-1
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
Ziyang Ma-;E. Wu
中科院分区：
文献类型：
--
作者：
Ziyang Ma-;E. Wu
在本文中，我们介绍了一种新颖、实时且强大的手部跟踪系统，能够使用单个深度相机在全自由度（DOF）下跟踪关节式手部运动。与大多数以前的系统不同，我们的系统能够自动初始化并从跟踪丢失中恢复。这是通过本文提出的高效的两阶段k最近邻数据库搜索方法来实现的。它对于从小手部深度图像的预渲染数据库中进行搜索非常有效，旨在为基于模型的跟踪提供良好的初始猜测。我们还提出了一个鲁棒的目标函数，并在基于模型的跟踪中使用基于重采样的策略改进了粒子群优化算法。它比以前的方法更有效地在全自由度手部运动空间中提供连续解决方案。我们的系统在 GeForce GTX 580 GPU 上以 40 fps 的速度运行，实验结果表明，该系统在速度和准确性方面均优于基于最先进模型的手部跟踪系统。该工作成果对于人机交互和虚拟现实领域的各种应用具有重要意义。
In this paper, we introduce a novel, real-time and robust hand tracking system, capable of tracking the articulated hand motion in full degrees of freedom (DOF) using a single depth camera. Unlike most previous systems, our system is able to initialize and recover from tracking loss automatically. This is achieved through an efficient two-stagek-nearest neighbor database searching method proposed in the paper. It is effective for searching from a pre-rendered database of small hand depth images, designed to provide good initial guesses for model based tracking. We also propose a robust objective function, and improve the Particle Swarm Optimization algorithm with a resampling based strategy in model based tracking. It provides continuous solutions in full DOF hand motion space more efficiently than previous methods. Our system runs at 40 fps on a GeForce GTX 580 GPU and experimental results show that the system outperforms the state-of-the-art model based hand tracking systems in terms of both speed and accuracy. The work result is of significance to various applications in the field of human–computer-interaction and virtual reality.