Human Motion Recognition Using Zero-Shot Learning

Abstract

In this study, we use motion recognition to recognize unseen and unlabeled movement patterns, which are widely used and challenging in machine learning. Motion recognition tackles some of the emerging challenges in computer vision problems, such as analyzing actions in a surveillance video where there is a lack of sufficient training data. Motion recognition also plays a pivotal role in human action and behavior recognition. In this paper, we propose a novel action and motion recognition method using zero-shot learning. We overcome a limitation of machine learning by recognizing unseen and unlabeled classes in the field of human action recognition. In order to evaluate the effectiveness of the proposed solution, we use a dataset available from the UCI machine learning repository. This dataset enables us to apply zero-shot learning to human motion and action recognition. Our results verify that the proposed method outperforms state-of-the-art algorithms.