Motion Object Feature Extraction Method Based on Multi-feature Fusion

Authors

Xidao Luan, 1. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410003, China; ;
Yuxiang Xie, 2. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China;
Zhang Xin, 2. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China;
Niu Xiao, 2. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China;

Abstract

Abstract: Motion object feature extraction is the basis of motion object classification. Traditionally motion object classification mainly depends on single feature extraction which is sensitive to the aspects like motion object detection area, angle, scale and noise disturbance, thus decreases the classification efficiency. To solve these problems and improve the robustness of the algorithms, a motion object feature extraction method based on multi-feature fusion was proposed. In this method, width height ratio feature, rotation invariant uniform local binary pattern feature and SIFT feature were considered, and by fusing them into the SVM and KNN classifier, motion object classification was carried out. Experiments prove that the motion object feature extraction method can greatly improve the average classification precision.

Recommended Citation

Luan, Xidao; Xie, Yuxiang; Xin, Zhang; and Xiao, Niu (2020) "Motion Object Feature Extraction Method Based on Multi-feature Fusion," Journal of System Simulation: Vol. 29: Iss. 6, Article 20.
DOI: 10.16182/j.issn1004731x.joss.201706020
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss6/20

First Page

1304

Revised Date

2016-05-18

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201706020

Last Page

1310

CLC

TP39

Recommended Citation

Luan Xidao, Xie Yuxiang, Zhang Xin, Niu Xiao. Motion Object Feature Extraction Method Based on Multi-feature Fusion[J]. Journal of System Simulation, 2017, 29(6): 1304-1310.

Corresponding Author

Yuxiang Xie,

DOI

10.16182/j.issn1004731x.joss.201706020