Abstract
The general ability to analyze and classify the 3D kinematics of the human form is an essential step in the development of socially adept humanoid robots. A variety of different types of signals can be used by machines to represent and characterize actions such as RGB videos, infrared maps, and optical flow. In particular, skeleton sequences provide a natural 3D kinematic description of human motions and can be acquired in real time using RGB+D cameras. Moreover, skeleton sequences are generalizable to characterize the motions of both humans and humanoid robots. The Globally Optimal Reparameterization Algorithm (GORA) is a novel, recently proposed algorithm for signal alignment in which signals are reparameterized to a globally optimal universal standard timescale (UST). Here, we introduce a variant of GORA for humanoid action recognition with skeleton sequences, which we call GORA-S. We briefly review the algorithm's mathematical foundations and contextualize them in the problem of action recognition with skeleton sequences. Subsequently, we introduce GORA-S and discuss parameters and numerical techniques for its effective implementation. We then compare its performance with that of the DTW and FastDTW algorithms, in terms of computational efficiency and accuracy in matching skeletons. Our results show that GORA-S attains a complexity that is significantly less than that of any tested DTW method. In addition, it displays a favorable balance between speed and accuracy that remains invariant under changes in skeleton sampling frequency, lending it a degree of versatility that could make it well-suited for a variety of action recognition tasks.
Abstract (translated)
分析和分类人体三维运动学的一般能力是社会娴熟的人形机器人发展的重要一步。机器可以使用各种不同类型的信号来表示和表征诸如RGB视频，红外图和光流之类的动作。特别地，骨架序列提供人类运动的自然3D运动学描述，并且可以使用RGB + D相机实时获取。此外，骨架序列可推广用于表征人类和类人机器人的运动。全局最优重新参数化算法（GORA）是最近提出的一种新颖的信号对准算法，其中信号被重新参数化为全局最优通用标准时标（UST）。在这里，我们介绍了GORA的变体，用于人体动作识别和骨架序列，我们称之为GORA-S。我们简要回顾一下算法的数学基础，并用骨架序列对动作识别问题进行语境化。随后，我们介绍了GORA-S并讨论了有效实施的参数和数值技术。然后，我们将其性能与DTW和FastDTW算法的性能进行比较，在计算效率和匹配骨架的准确性方面。我们的结果表明，GORA-S的复杂性远远低于任何测试的DTW方法。此外，它在速度和精度之间显示出良好的平衡，在骨架采样频率的变化下保持不变，使其具有一定程度的多功能性，使其非常适合各种动作识别任务。
URL
https://arxiv.org/abs/1807.07432
