Online continuous motion recognition is a hot topic of research since it is more practical in real life application cases. Recently, Skeleton-based approaches have become increasingly popular, demonstrating the power of using such 3D temporal data. However, most of these works have focused on segment-based recognition and are not suitable for the online scenarios. In this paper, we propose an online recognition system for skeleton sequence streaming composed from two main components: a detector and a classifier, which use a Semi-Positive Definite (SPD) matrix representation and a Siamese network. The powerful statistical representations for the skeletal data given by the SPD matrices and the learning of their semantic similarity by the Siamese network enable the detector to predict time intervals of the motions throughout an unsegmented sequence. In addition, they ensure the classifier capability to recognize the motion in each predicted interval. The proposed detector is flexible and able to identify the kinetic state continuously. We conduct extensive experiments on both hand gesture and body action recognition benchmarks to prove the accuracy of our online recognition system which in most cases outperforms state-of-the-art performances.

翻译：在线连续运动识别是当前研究的热点，因其在实际应用场景中更具实用性。近年来，基于骨架的方法日益流行，展现了利用此类三维时序数据的强大能力。然而，多数现有工作聚焦于基于片段的识别，并不适用于在线场景。本文提出一种面向骨架序列流的在线识别系统，该系统由两个核心组件构成：检测器与分类器，二者均采用半正定矩阵表示与孪生网络。SPD矩阵为骨架数据提供的强大统计表征，结合孪生网络对其语义相似性的学习，使检测器能够预测未分割序列中动作发生的时间区间。此外，这些技术确保了分类器对每个预测区间内动作的识别能力。所提出的检测器具有灵活性，可连续识别运动状态。我们在手势识别与身体动作识别基准数据集上进行了大量实验，验证了本在线识别系统的准确性，其在多数情况下超越了现有最优性能。