Dance Style Classification using Laban-Inspired and Frequency-Domain Motion Features

TL;DR

This paper introduces a lightweight, interpretable framework for classifying dance styles from video-based pose data using Laban-inspired spatial-temporal features and frequency domain analysis, achieving robust results with low computational cost.

Contribution

It presents a novel combination of Laban-inspired motion features and frequency domain analysis for dance style classification, emphasizing interpretability and efficiency.

Findings

Effective classification of dance styles using proposed features

Low computational complexity of the framework

Features capture stylistic nuances accurately

Abstract

Dance is an essential component of human culture and serves as a tool for conveying emotions and telling stories. Identifying and distinguishing dance genres based on motion data is a complex problem in human activity recognition, as many styles share similar poses, gestures, and temporal motion patterns. This work presents a lightweight framework for classifying dance styles that determines motion characteristics based on pose estimates extracted from videos. We propose temporal-spatial descriptors inspired by Laban Movement Analysis. These features capture local joint dynamics such as velocity, acceleration, and angular movement of the upper body, enabling a structured representation of spatial coordination. To further encode rhythmic and periodic aspects of movement, we integrate Fast Fourier Transform features that characterize movement patterns in the frequency domain. The proposed approach achieves robust classification of different dance styles with low computational effort, as complex model architectures are not required, and shows that interpretable motion representations can effectively capture stylistic nuances.