Multi-modal action segmentation in the kitchen with a feature fusion approach

In this paper, we propose a "Multi-modal Action Segmentation approach"that uses three modalities: (i) video, (ii) audio, (iii) thermal to classify cooking behavior in the kitchen. These 3 modalities are assumed to be features related to cooking. However, there is no public dataset containing these three modalities. Therefore, we built the original dataset and frame-level annotation. We then examined the usefulness of Action Segmentation using multi-modal features. We analyzed the effects of each modality using three evaluation metrics. As a result, the accuracy, edit distance, and F1 value were improved by up to about 1%, 2%, and 8%, respectively, compared to the case when only images were used.