[1]韩欣欣,叶奇玲.基于 SIFT 和 HOG 特征融合的人体行为识别方法[J].计算机技术与发展,2019,29(06):71-73.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 015]
　HAN Xin-xin,YE Qi-ling.Human Action Recognition Based on Feature Fusion of SIFT and HOG[J].,2019,29(06):71-73.[doi:10. 3969 / j. issn. 1673-629X. 2019. 06. 015]
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基于 SIFT 和 HOG 特征融合的人体行为识别方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年06期
页码:
71-73
栏目:
智能、算法、系统工程
出版日期:
2019-06-10

文章信息/Info

Title:
Human Action Recognition Based on Feature Fusion of SIFT and HOG
文章编号:
1673-629X(2019)06-0071-03
作者:
韩欣欣; 叶奇玲
南京邮电大学 通信工程学院,江苏 南京 210003
Author(s):
HAN Xin-xin; YE Qi-ling
School of Communication Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
行为识别; 特征融合; 尺度不变特征变换; 方向梯度直方图; 支持向量机
Keywords:
action recognition; features fusion; SIFT; HOG; support vector machine
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 06. 015
摘要:
行为识别是视频分析的一个核心任务,而行为特征的提取与选择直接影响识别效果。 针对单一特征往往受到人体外观、环境、摄像机设置等因素的影响而识别效果不佳的问题,提出一种分别提取尺度不变特征变换(SIFT)和方向梯度直方图(HOG)的特征并形成融合特征,再利用支持向量机(SVM)完成特征分类的行为识别方法。 基于 Matlab 人体行为识别和检测的研究,通过采用 KTH 和 Weizmann 人体行为库来验证该算法的有效性。 实验结果表明,该算法在人体行为识别中识别率可达到 90%以上,比单独使用上述两种特征或者其他传统的描述子更高效,同时也能更好地适应光照等外部因素的变化,得到更好的识别率。
Abstract:
Action recognition is a core task of video analysis,and the extraction and selection of behavior features directly affect the recognition effect. In view of the problem of bad recognition effect for single feature caused by human appearance,environment,camera settings and other factors,we propose a action recognition method which respectively extracts scale-invariant feature transform (SIFT) and direction gradient histogram (HOG) and fuse them,and then adopts support vector machine (SVM) for feature classification. Based on research on Matlab human action recognition and detection,the validity of the algorithm is verified by KTH and Weizmann human behavior libraries. Experiment shows that the recognition rate of the algorithm in human action recognition can reach more than 90%, which is more efficient than above two features alone or other traditional descriptors,and can better adapt to the change of external factors such as illumination and get better recognition rate.

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更新日期/Last Update: 2019-06-10