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Mathematical Problems in Engineering
Volume 2015, Article ID 676090, 9 pages
http://dx.doi.org/10.1155/2015/676090
Research Article

Human Activity Recognition as Time-Series Analysis

Department of Computer Science, Kyonggi University, San 94-6, Yiui-Dong, Youngtong-Gu, Suwon-Si 443-760, Republic of Korea

Received 6 June 2015; Revised 26 August 2015; Accepted 27 August 2015

Academic Editor: Meng Du

Copyright © 2015 Hyesuk Kim and Incheol Kim. 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

We propose a system that can recognize daily human activities with a Kinect-style depth camera. Our system utilizes a set of view-invariant features and the hidden state conditional random field (HCRF) model to recognize human activities from the 3D body pose stream provided by MS Kinect API or OpenNI. Many high-level daily activities can be regarded as having a hierarchical structure where multiple subactivities are performed sequentially or iteratively. In order to model effectively these high-level daily activities, we utilized a multiclass HCRF model, which is a kind of probabilistic graphical models. In addition, in order to get view-invariant, but more informative features, we extract joint angles from the subject’s skeleton model and then perform the feature transformation to obtain three different types of features regarding motion, structure, and hand positions. Through various experiments using two different datasets, KAD-30 and CAD-60, the high performance of our system is verified.