Comparison of Deep Learning Techniques for Video-Based Automatic Recognition of Greek Folk Dances.

Folk dances consist an important part of the Intangible Cultural Heritage (ICH) of each place. Nowadays, there is a great amount of videos related to folk dances. An automatic dance recognition algorithm can ease the management of this content and enforce the promotion of folk dances to the younger generations. Automatic dance recognition is still an open research area that belongs to the more general field of human activity recognition. Our work focuses on the exploration of existing deep neural network architectures for automatic recognition of Greek folk dances depicted in standard videos, as well as the experimentation with different representations of input. For our experiments, we have collected YouTube videos of Greek folk dances from north-eastern Greece. Specifically, we have validated three different deep neural network architectures using raw RGB and grayscale video frames, optical flow, as well as "visualised" multi-person 2D poses. In this paper, we describe our experiments, and, finally, we present the results and findings of the conducted research.