An Automatic Detection of Fundamental Postures in Vietnamese Traditional Dances

Abstract

Preserving and promoting the intangible cultural heritage is one of the essential problems of interest. In addition, the cultural heritage of the world has been accumulated and early respected during the development of human society. For preservation of traditional dances, this paper is one of the significant processed steps in our research sequence to build an intelligent storage repository that would help to manage the large-scale heterogeneous digital contents efficiently, particularly in dance domain. We concentrated on classifying the fundamental movements of Vietnamese Traditional Dances (VTDs), which are the foundations of automatically detecting the motions of the dancer's body parts. Moreover, we also propose a framework to classify basic movements through coupling a sequential aggregation of the Deep-CNN architectures (to extract the features) and Support Vector Machine (to classify the movements). In this study, we detect and extract automatically the primary movements of VTDs, we then store the extracted concepts into an ontology that serves for reasoning, queryanswering, and searching dance videos.