论文标题
探索子骨骼轨迹，以解释手语的可解释识别
Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language
论文作者
论文摘要
跟踪传感器和构成估算软件的最新进展使智能系统能够使用骨架联合位置的轨迹进行监督学习。我们研究了准确识别手语单词的问题，这是缩小听力人和不恐怖之间的沟通差距的关键。 我们的方法探索了我们称之为运动的“子骨骼”方面的几何特征空间。我们使用自然的，速度不变的距离度量来评估特征空间轨迹的相似性，从而实现清晰而有见地的最近的邻居分类。我们的基本方法的简单性和可扩展性允许在几乎没有参数调整的不同数据域中立即应用。 我们通过对来自不同应用领域和跟踪技术的数据进行了实验，证明了我们的基本方法的有效性以及增强的变化。令人惊讶的是，我们的简单方法改善了对最近最先进的方法的标志识别。
Recent advances in tracking sensors and pose estimation software enable smart systems to use trajectories of skeleton joint locations for supervised learning. We study the problem of accurately recognizing sign language words, which is key to narrowing the communication gap between hard and non-hard of hearing people. Our method explores a geometric feature space that we call `sub-skeleton' aspects of movement. We assess similarity of feature space trajectories using natural, speed invariant distance measures, which enables clear and insightful nearest neighbor classification. The simplicity and scalability of our basic method allows for immediate application in different data domains with little to no parameter tuning. We demonstrate the effectiveness of our basic method, and a boosted variation, with experiments on data from different application domains and tracking technologies. Surprisingly, our simple methods improve sign recognition over recent, state-of-the-art approaches.
