Table of Contents Author Guidelines Submit a Manuscript
Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 5861435, 11 pages
https://doi.org/10.1155/2017/5861435
Research Article

Mining Key Skeleton Poses with Latent SVM for Action Recognition

1School of Computer Engineering and Science, Shanghai University, Shanghai, China
2School of Mathematic and Statistics, Nanyang Normal University, Nanyang, China

Correspondence should be addressed to Xiaoqiang Li; nc.ude.uhs.i@ilqx and Dong Liao; nc.ude.unyn@gnodoail

Received 23 August 2016; Revised 8 November 2016; Accepted 15 December 2016; Published 23 January 2017

Academic Editor: Lei Zhang

Copyright © 2017 Xiaoqiang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Human action recognition based on 3D skeleton has become an active research field in recent years with the recently developed commodity depth sensors. Most published methods analyze an entire 3D depth data, construct mid-level part representations, or use trajectory descriptor of spatial-temporal interest point for recognizing human activities. Unlike previous work, a novel and simple action representation is proposed in this paper which models the action as a sequence of inconsecutive and discriminative skeleton poses, named as key skeleton poses. The pairwise relative positions of skeleton joints are used as feature of the skeleton poses which are mined with the aid of the latent support vector machine (latent SVM). The advantage of our method is resisting against intraclass variation such as noise and large nonlinear temporal deformation of human action. We evaluate the proposed approach on three benchmark action datasets captured by Kinect devices: MSR Action 3D dataset, UTKinect Action dataset, and Florence 3D Action dataset. The detailed experimental results demonstrate that the proposed approach achieves superior performance to the state-of-the-art skeleton-based action recognition methods.