融合多特征的运动目标特征提取方法

doi:10.16182/j.issn1004731x.joss.201706020

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (6): 1304-1310.doi: 10.16182/j.issn1004731x.joss.201706020

融合多特征的运动目标特征提取方法

栾悉道¹, 谢毓湘^2,*, 张芯², 牛晓²

1.长沙大学数学与计算机科学系,湖南长沙 410003;
2.国防科学技术大学信息系统与管理学院,湖南长沙 410073

收稿日期:2016-01-29 修回日期:2016-05-18 出版日期:2017-06-08 发布日期:2020-06-04
作者简介:栾悉道(1976-),男,山东即墨,博士,副教授,研究方向为多媒体信息系统。
基金资助:
国家自然科学基金(61571453), 湖南省自然科学基金(14JJ3010), 湖南省教育厅重点项目(15A020)

Motion Object Feature Extraction Method Based on Multi-feature Fusion

Luan Xidao¹, Xie Yuxiang^2,*, Zhang Xin², Niu Xiao²

1. College of Computer Engineering and Applied Mathematics, Changsha University, Changsha 410003, China;
2. College of Information Systems and Management, National University of Defense Technology, Changsha 410073, China

Received:2016-01-29 Revised:2016-05-18 Online:2017-06-08 Published:2020-06-04

摘要/Abstract

摘要： 运动目标的特征提取是运动目标分类的基础。仅依赖于单特征进行运动目标分类容易受到目标区域检测精度不准确、目标角度、尺度变化以及噪声干扰等因素影响,从而造成分类准确度降低。为克服上述缺点,提高算法的鲁棒性,提出了融合宽高比特征、旋转不变均匀局部二值模式特征以及尺度不变特征(SIFT特征)的运动目标特征提取算法,并在此基础上基于支撑向量机和K近邻方法对运动目标进行分类。实验表明,采用融合多特征的运动目标特征提取方法能够显著提高运动目标的平均分类准确率。

关键词: 运动目标, 特征提取, 局部二值模式, 尺度不变特征SIFT

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.

Key words: motion object, feature extraction, local binary pattern, scale-invariant feature transform (SIFT)

中图分类号:

TP39

栾悉道, 谢毓湘, 张芯, 牛晓. 融合多特征的运动目标特征提取方法[J]. 系统仿真学报, 2017, 29(6): 1304-1310.

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.

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融合多特征的运动目标特征提取方法

Motion Object Feature Extraction Method Based on Multi-feature Fusion

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