Publication details

Efficient Indexing of 3D Human Motions

Investor logo


Year of publication 2021
Type Article in Proceedings
Conference ACM International Conference on Multimedia Retrieval (ICMR)
MU Faculty or unit

Faculty of Informatics

Keywords human motion data; skeleton sequences; motion word; text-based processing; indexing; extended inverted files; ranked retrieval; approximate searching; scalability
Description Digitization of human motion using 2D or 3D skeleton representations offers exciting possibilities for many applications but, at the same time, requires scalable content-based retrieval techniques to make such data reusable. Although a lot of research effort focuses on extracting content-preserving motion features, there is a lack of techniques that support efficient similarity search on a large scale. In this paper, we introduce a new indexing scheme for organizing large collections of spatio-temporal skeleton sequences. Specifically, we apply the motion-word concept to transform skeleton sequences into structured text-like motion documents, and index such documents using an extended inverted-file approach. Over this index, we design a new similarity search algorithm that exploits the properties of the motion-word representation and provides efficient retrieval with a variable level of approximation, possibly reaching constant search costs disregarding the collection size. Experimental results confirm the usefulness of the proposed approach.
Related projects:

You are running an old browser version. We recommend updating your browser to its latest version.

More info