The Complex Action Recognition via the Correlated Topic Model
Joint Authors
Wang, Zheng-wu
Tu, Hong-bin
Xia, Li-min
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-01-16
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Tu, Hong-bin& Xia, Li-min& Wang, Zheng-wu. 2014. The Complex Action Recognition via the Correlated Topic Model. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051140
Modern Language Association (MLA)
Tu, Hong-bin…[et al.]. The Complex Action Recognition via the Correlated Topic Model. The Scientific World Journal No. 2014 (2014), pp.1-10.
https://search.emarefa.net/detail/BIM-1051140
American Medical Association (AMA)
Tu, Hong-bin& Xia, Li-min& Wang, Zheng-wu. The Complex Action Recognition via the Correlated Topic Model. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-10.
https://search.emarefa.net/detail/BIM-1051140
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051140