Physics-based keyframe selection for human motion summarization

Analysis of human motion is a field of research that attracts significant interest because of the wide range of associated application domains. Intangible Cultural Heritage (ICH), including the performing arts and in particular dance, is one of the domains where related research is especially useful and challenging. Effective keyframe selection from motion sequences can provide an abstract and compact representation of the semantic information encoded therein, contributing towards useful functionality, such as fast browsing, matching and indexing of ICH content. The availability of powerful 3D motion capture sensors along with the fact that video summarization techniques are not always applicable to the particular case of dance movement create the need for effective and efficient summarization techniques for keyframe selection from 3D human motion capture data sequences. In this paper, we introduce two techniques: a "time-independent" method based on k-means++ clustering algorithm for the extraction of prominent representative instances of a dance, and a physics-based technique that creates temporal summaries of the sequence at different levels of detail. The proposed methods are evaluated on two dance motion datasets and show promising results.