Segmentation and Classification of Human Actions and Actor Characteristics with 3d Motion Data

Abstract

In this paper we have used 3D motion capture data with the aim of detecting and classifying specific human actions. In addition to recognition of basic action classes, actor styles and characteristics such as gender, age, and energy level have also beensubject to classification. We have applied and compared three main methods: nearest neighbour search, hidden Markov models, and artificial neural networks. Using these techniques, we have proposed exhaustive algorithms for detection of actions in a motion piece and subsequently classifying the segmented actions and respective characteristics of the actors. We have tested the methods for various sequences and compared the results for a comprehensive evaluation of each of the proposed techniques.Our findings can be largely used for general classification of human motion data for multimedia applications as well assorting and classifying data sets of human motion data especially those acquired using visual marker-based motion capture systemssuch as the one employed in this research.