This work targets Dance Style Recognition in videos as an application of Human Action Recognition. We propose a novel Spatio-Temporal Laban Feature descriptor (STLF) for dance style recognition based on Laban theory. Laban Movement Analysis has become increasingly popular as a language to describe, index and record human motion. We only exploit motion features and body-pose information without encoding the appearance. The model is tested on some action recognition benchmarks and ICD, a challenging dataset of YouTube dance videos. Unlike other works, where Laban based features have been used in constrained environments, with static camera, sensors and no background noise, we employ STLF on videos in unconstrained and natural settings. It is robust to camera jitter, zoom variations and other acquisition conditions and is computationally cheap. It performs comparable or better than the state-of-the-art.
Published in: 2018 24th International Conference on Pattern Recognition (ICPR)