Human Activity Recognition Based on Weighted Sum Method and Combination of Feature Extraction Methods
International Journal of Intelligent Information Systems
Volume 7, Issue 1, February 2018, Pages: 9-14
Received: Apr. 26, 2018; Accepted: May 24, 2018; Published: Jun. 13, 2018
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Authors
Nang Hung Van Nguyen, Faculty of Information Technology, Danang University of Science and Technology, Danang, Vietnam
Minh Tuan Pham, Faculty of Information Technology, Danang University of Science and Technology, Danang, Vietnam
Nho Dai Ung, Faculty of Information Technology, Danang University of Science and Technology, Danang, Vietnam
Kanta Tachibana, Faculty of Informatics, Department of Information System and Applied Mathematecs, Kogakuin University, Tokyo, Japan
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Abstract
Human Activity Recognition (HAR) is one of the most important areas of computer vision research. The biggest difficulty for HAR system is that the camera could only film in one direction, leading to a shortage of data and low recognition results. This paper focuses on researching and building new models of HAR, including Principal Components Analysis (PCA), Linear discriminant Analysis (LDA) is to reduce the dimensionality and size of data, contributing to high recognition accuracy. First, from the 3D motion data, we conducted a pretreatment and feature extraction of objects. Next, we built a recognition model corresponding to each feature extraction method and we used Support Vector Machine (SVM) model to train. Finally, we used weighted methods to combine the results of the model to train and give the final results. The paper experiment on CMU MOCAP database and the percentage receiving proposed method is higher than that from the previous method.
Keywords
Human Activity Recognition, Principal Components Analysis, Linear Discriminant Analysis, Support Vector Machine
To cite this article
Nang Hung Van Nguyen, Minh Tuan Pham, Nho Dai Ung, Kanta Tachibana, Human Activity Recognition Based on Weighted Sum Method and Combination of Feature Extraction Methods, International Journal of Intelligent Information Systems. Vol. 7, No. 1, 2018, pp. 9-14. doi: 10.11648/j.ijiis.20180701.13
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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