Wu Ying, Lin John, Huang Thomas S
Department of Electrical and Computer Engineering, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA.
IEEE Trans Pattern Anal Mach Intell. 2005 Dec;27(12):1910-22. doi: 10.1109/TPAMI.2005.233.
Capturing the human hand motion from video involves the estimation of the rigid global hand pose as well as the nonrigid finger articulation. The complexity induced by the high degrees of freedom of the articulated hand challenges many visual tracking techniques. For example, the particle filtering technique is plagued by the demanding requirement of a huge number of particles and the phenomenon of particle degeneracy. This paper presents a novel approach to tracking the articulated hand in video by learning and integrating natural hand motion priors. To cope with the finger articulation, this paper proposes a powerful sequential Monte Carlo tracking algorithm based on importance sampling techniques, where the importance function is based on an initial manifold model of the articulation configuration space learned from motion-captured data. In addition, this paper presents a divide-and-conquer strategy that decouples the hand poses and finger articulations and integrates them in an iterative framework to reduce the complexity of the problem. Our experiments show that this approach is effective and efficient for tracking the articulated hand. This approach can be extended to track other articulated targets.
从视频中捕捉人类手部运动涉及到对刚性全局手部姿态以及非刚性手指关节运动的估计。关节式手部的高自由度所带来的复杂性对许多视觉跟踪技术提出了挑战。例如，粒子滤波技术受到大量粒子的苛刻要求和粒子退化现象的困扰。本文提出了一种通过学习和整合自然手部运动先验知识来跟踪视频中关节式手部的新方法。为了应对手指关节运动，本文基于重要性采样技术提出了一种强大的序贯蒙特卡罗跟踪算法，其中重要性函数基于从运动捕捉数据中学习到的关节配置空间的初始流形模型。此外，本文提出了一种分而治之的策略，将手部姿态和手指关节运动解耦，并将它们整合到一个迭代框架中以降低问题的复杂性。我们的实验表明，这种方法对于跟踪关节式手部是有效且高效的。这种方法可以扩展到跟踪其他关节式目标。
