Hierarchical Sparse Modeling for Representative Selection in Choreographic Time Series

摘要

In this paper, we propose a novel method to extract representative instances from choreographic sequences of 3D human motion data. The proposed key-frame extraction method implements a hierarchical scheme that exploits spatio-temporal variations of the dance movement features. The method is based on a hierarchical adaptation of the sparse modeling for representative selection algorithm (SMRS). Leveraging a joint -centric distance metric, summaries are provided at variable levels of granularity depending on the richness and complexity of the visual content at different sequence segments. The proposed method can contribute to addressing the need of organizing, indexing, archiving, retrieving and analyzing intangible (in this case, dance-related) cultural content in a tractable fashion and with lower computational and storage resource requirements. The approach is evaluated on real-world dance sequences, as well as on theatrical kinesiology datasets (available by Carnegie Mellon University). Comparisons with traditional video summarization methods show that the proposed hierarchical spatio- temporal decomposition scheme achieves promising results.