A kinetic energy-based feature for unsupervised motion clustering

Abstract

Motion databases usually contain sequences of movements and searching these vast databases is not an easy task. Motion clustering can reduce this difficulty by grouping sample movements into various motion groups containing similar actions. The pose distance is often used as a feature during motion-clustering tasks. However, the main weakness of this strategy is its computational complexity. Query motions are also required to cluster motion sequences. To address these problems, we propose a motion-clustering algorithm based on the use of kinetic energy to cluster sample motions. Our method does not require query motions during the clustering process, so the clustering results can be generated without supervision. Our experimental results confirmed that our proposed method delivered comparable performance to pose distance-based methods, while its computational complexity was significantly lower than that of existing methods.