Behavioral segmentation for human motion capture data based on graph cut method. (December 2017)

• Record Type: Journal Article
• Authors: Yu, Xiaomin; Liu, Weibin; Xing, Weiwei
• Is Part Of: Journal of visual languages & computing. Volume 43(2017)
• Page Start: 50
• Page End: 59
• Publication Date: 2017-12
• Subjects: Motion capture data -- Behavioral segmentation -- Graph cut -- Spectral clustering; Visual programming languages (Computer science) -- Periodicals; Visual programming (Computer science) -- Periodicals; Programming languages (Electronic computers) -- Semantics -- Periodicals; Langages de programmation visuelle -- Périodiques; Programmation visuelle -- Périodiques; Langages de programmation -- Sémantique -- Périodiques; Programming languages (Electronic computers) -- Semantics; Visual programming (Computer science); Visual programming languages (Computer science); Periodicals; Electronic journals; 005
• DOI: 10.1016/j.jvlc.2017.09.001
• Languages: English
• ISSNs: 1045-926X

Abstract

Abstract: With the development of human motion capture, realistic human motion capture data has been widely implemented to many fields. However, segmenting motion capture data sequences manually into distinct behavior is time-consuming and laborious. In this paper, we introduce an efficient unsupervised method based on graph partition for automatically segmenting motion capture data. For N-Frame motion capture data sequence, we construct an undirected, weighted graph G = G ( V, E ), where the node set V represent frames of motion sequence and the weight of the edge set E describes similarity between frames. In this way, behavioral segmentation problem can be transformed into graph cut problem. However, traditional graph cut problem is NP hard. By analyzing the relationship between graph cut and spectral clustering, we apply spectral clustering to the NP hard problem of graph cut. In this paper, two methods of spectral clustering, t-nearest neighbors and the Nystrom method, are employed to cluster motion capture data for getting behavioral segmentation. In addition, we define an energy function to refine the results of behavioral segmentation. Extensive experiments are conducted on the dataset of multi-behavior motion capture data from CMU database. The experimental results prove that our novel method is robust and effective.