基于SPD矩阵与孪生网络的在线动作识别系统，实现连续流式数据的精准动作检测与分类。
Accurate online action and gesture recognition system using detectors and Deep SPD Siamese Networks
- 用SPD矩阵表示骨骼序列，结合孪生网络学习语义相似性
- 在无分段连续序列上实现动作区间的精准预测，准确率超现有方法
- 适用于实时手部手势与身体动作识别，适合实际应用场景
在线连续动作识别因在真实场景中更具实用性而成为研究热点。近年来，基于骨架的方法因其利用3D时序数据的能力备受关注。然而，多数工作聚焦于分段识别，难以适应在线场景。本文提出一种由检测器与分类器组成的在线识别系统，用于处理骨架序列流。系统采用半正定（SPD）矩阵表示与孪生网络，利用SPD矩阵对骨骼数据的强大统计表征能力，以及孪生网络对语义相似性的学习，使检测器可在未分段序列中持续预测动作发生的时间区间，并确保分类器在每个预测区间内准确识别动作。所提检测器具备灵活性，可连续识别运动状态。我们在手部手势与身体动作识别基准上进行了大量实验，结果表明该系统在多数情况下优于当前最优性能。
原文摘要 · Abstract (English)
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.
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