Institute of Computer Science, University of Rostock, 18059 Rostock, Germany.
Institute of Communications Engineering, University of Rostock, 18119 Rostock, Germany.
Sensors (Basel). 2018 Aug 23;18(9):2778. doi: 10.3390/s18092778.
Providing ground truth is essential for activity recognition and behaviour analysis as it is needed for providing training data in methods of supervised learning, for providing context information for knowledge-based methods, and for quantifying the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label, enabling further reasoning. In this paper, we present a novel approach to semantic annotation by means of plan operators. We provide a step by step description of the workflow to manually creating the ground truth annotation. To validate our approach, we create semantic annotation of the Carnegie Mellon University (CMU) grand challenge dataset, which is often cited, but, due to missing and incomplete annotation, almost never used. We show that it is possible to derive hidden properties, behavioural routines, and changes in initial and goal conditions in the annotated dataset. We evaluate the quality of the annotation by calculating the interrater reliability between two annotators who labelled the dataset. The results show very good overlapping (Cohen's κ of 0.8) between the annotators. The produced annotation and the semantic models are publicly available, in order to enable further usage of the CMU grand challenge dataset.
提供地面实况对于活动识别和行为分析至关重要，因为它是监督学习方法提供训练数据、为基于知识的方法提供上下文信息以及量化识别性能所必需的。语义注释通过为标签赋予语义含义来扩展简单的符号标签，从而实现进一步的推理。在本文中，我们提出了一种通过算子进行语义注释的新方法。我们提供了手动创建地面实况注释的工作流程的逐步描述。为了验证我们的方法，我们创建了卡内基梅隆大学（CMU）大挑战数据集的语义注释，该数据集经常被引用，但由于缺少和不完整的注释，几乎从未被使用过。我们表明，在注释数据集中可以推导出隐藏的属性、行为例程以及初始和目标条件的变化。我们通过计算标记数据集的两个注释者之间的评分者间可靠性来评估注释的质量。结果表明，注释者之间的重叠非常好（Cohen 的 κ 为 0.8）。生成的注释和语义模型是公开的，以便能够进一步使用 CMU 大挑战数据集。
