论文标题
通过贝叶斯过滤中的贝叶斯滤波器中的活动识别的活动识别
Activity Recognition in Assembly Tasks by Bayesian Filtering in Multi-Hypergraphs
论文作者
论文摘要
我们在手动工作过程中研究基于传感器的人类活动识别，例如组装任务。在这样的过程中，系统状态通常具有丰富的结构，涉及对象属性和关系。因此，由于系统状态数量的组合爆炸，通过递归贝叶斯过滤估算传感器观察的隐藏系统状态可能非常具有挑战性。为了减轻此问题，我们为此类过程提出了一个有效的贝叶斯过滤模型。在我们的方法中，系统状态由多hypergraphs表示，并且系统动力学以图形重写规则进行建模。我们展示了一个初步概念，该概念允许比全枚举更紧凑地代表分布，并提出直接适用于这种紧凑表示的推理算法。我们演示了该算法在真实数据集上的适用性。
We study sensor-based human activity recognition in manual work processes like assembly tasks. In such processes, the system states often have a rich structure, involving object properties and relations. Thus, estimating the hidden system state from sensor observations by recursive Bayesian filtering can be very challenging, due to the combinatorial explosion in the number of system states. To alleviate this problem, we propose an efficient Bayesian filtering model for such processes. In our approach, system states are represented by multi-hypergraphs, and the system dynamics is modeled by graph rewriting rules. We show a preliminary concept that allows to represent distributions over multi-hypergraphs more compactly than by full enumeration, and present an inference algorithm that works directly on this compact representation. We demonstrate the applicability of the algorithm on a real dataset.
