Towards Automated Generation of Semantic Annotation for Activity Recognition Problems

Ground truth is essential for activity recognition problems. It is used to apply methods of supervised learning, to provide context information for knowledge-based methods, and to quantify the recognition performance. Semantic annotation extends simple symbolic labelling by assigning semantic meaning to the label and enables reasoning about the semantic structure of the observed activity. The development of semantic annotation for activity recognition is a time consuming task, which involves a lot of effort and expertise. To reduce the time needed to develop semantic annotation, we propose an approach that automatically generates semantic models based on manually assigned symbolic labels. We provide a detailed description of the automated process for annotation generation and we discuss how it replaces the manual process. To validate our approach we compare automatically generated semantic annotation for the CMU grand challenge dataset with manual semantic annotation for the same dataset. The results show that automatically generated models are comparable to manually developed models but it takes much less time and no expertise in model development is required.