Extraction of key postures from 3D human motion data for choreography summarization

Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing and documentation of intangible cultural heritage assets. However, existing modelling approaches may involve huge amounts of information which are difficult to process, store and analyze. To address these limitations, usually a skeleton describing the dancer motion is extracted. However, often the complexity still remains due to the high spatio-temporal dependencies of the detected skeleton joints. In this paper, an alternative approach is presented: choreography summarization. This means that a very small number of image frames are extracted to represent a choreography, thus significantly reducing the processing and storage complexity. In our approach the problem of choreography summarization is treated as an unsupervised clustering approach. Evaluation indices are introduced for monitoring the summarization performance. Experimental results on real-life dancing performances verifies the capability of the proposed method to capture the main patterns of a choreography with a very small number of trajectory points.