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The Scientific World Journal
Volume 2014, Article ID 810185, 10 pages
http://dx.doi.org/10.1155/2014/810185
Research Article

The Complex Action Recognition via the Correlated Topic Model

1School of Information Science and Engineering, Central South University, ChangSha, Hunan 410075, China
2School of Traffic and Transportation Engineering, ChangSha University of Science & Technology, ChangSha, Hunan 410004, China

Received 1 October 2013; Accepted 5 December 2013; Published 16 January 2014

Academic Editors: F. Fernández de Vega and P.-A. Hsiung

Copyright © 2014 Hong-bin Tu 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 complex action recognition is an important research area of the action recognition. Among various obstacles to human complex action recognition, one of the most challenging is to deal with self-occlusion, where one body part occludes another one. This paper presents a new method of human complex action recognition, which is based on optical flow and correlated topic model (CTM). Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms of an occlusion state variable. Secondly, the structure from motion (SFM) is used for reconstructing the missing data of point trajectories. Then, we can extract the key frame based on motion feature from optical flow and the ratios of the width and height are extracted by the human silhouette. Finally, we use the topic model of correlated topic model (CTM) to classify action. Experiments were performed on the KTH, Weizmann, and UIUC action dataset to test and evaluate the proposed method. The compared experiment results showed that the proposed method was more effective than compared methods.