Spatio-Temporal Laban Features for Dance Style Recognition

Author

Swati Dewan ; Shubham Agarwal ; Navjyoti Singh

Abstract

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.

Source

Published in: 2018 24th International Conference on Pattern Recognition (ICPR)

Comments

ロバストであることを強調してる

読みたい、、

ラバン使ってる Feature extraction for human motion indexing of acted dance performances

URL

https://ieeexplore.ieee.org/document/8545251/keywords#keywords

Tag

#ICPR #2018

#laban_movement_analysis

#dance_style