Documenting Motion Sequences with a Personalized Annotation System

  • Authors:
  • Kanav Kahol;Priyamvada Tripathi;Sethuraman Panchanathan

  • Affiliations:
  • Arizona State University;Arizona State University;Arizona State University

  • Venue:
  • IEEE MultiMedia
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present a novel technique for motion annotation that adapts to a person's style and vocabulary of basic movements (gestures). The system segments continuous motion sequences into gestures, which it then documents in a personalized annotation with an intuitive hierarchical representation. Initial testing suggests that software based on this technique could be an effective teaching aid for dance and sports.