基于混合高斯模型和主成分分析的轨迹分析行为识别方法

doi:10.3969/j.issn.0372-2112.2016.01.021

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摘要

针对家庭辅助生活应用场景下的目标意图识别和异常行为判别问题,提出了一种基于目标轨迹的行为分析方法.首先,提出了关键点和关键区域的概念,将家庭环境划分为不同的关键点和关键区域,并以此来描述和区分不同轨迹;然后,提出了利用混合高斯模型的关键点及关键区域获取算法,将轨迹转化为关键点及关键区域表示,并以此为基础进行了行为意图的识别和部分异常轨迹的判断;最后,借助主成分分析的方法弥补混合高斯聚类在异常轨迹识别方面的缺陷,提高了识别准确率.实验表明,该方法能够有效的对行为意图和异常行为进行识别.

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	田国会
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	闫云章
	李国栋

关键词 ：意图识别, 异常行为检测, 轨迹分析, 混合高斯聚类, 主成分分析

Abstract：

In order to solve the problems of human motion intention recognition and abnormal behavior detection in home environment, a trajectory analysis based algorithm is discussed in this paper.Firstly, the home environment is divided into different key points and areas, so that the motion trajectory can be described by them.Moreover, based on mixture Gaussian model, the problems of motion intention recognition and abnormal behavior detection are analyzed.Finally, the PCA algorithm is applied to improve the accuracy of abnormal behavior detection.The experimental results show the effectiveness and reliability of the above conclusions.

Key words： motion intention recognition abnormal behavior detection motion trajectory analysis Gaussian mixture clustering principal component analysis

收稿日期: 2014-06-19 出版日期: 2014-10-31

ZTFLH:

TP391

基金资助:

国家自然科学基金(No.61203341,No.61075092);山东省自然科学基金(No.ZR2011FM011);山东省高等学校科技发展计划(No.J11LG01)

通讯作者: 尹建芹 E-mail: ise_yinjq@ujn.edu.cn

作者简介: 田国会 男,1969年8月出生,河北河间人,教授、博士生导师.1990年、1993年和1997年分别在山东大学、山东工业大学和东北大学获理学学士、工学硕士和工学博士学位.现为山东大学服务机器人研究室主任,主要从事服务机器人、智能空间等方面的研究. E-mail:g.h.tian@sdu.edu.cn

引用本文:

田国会, 尹建芹, 闫云章, 李国栋. 基于混合高斯模型和主成分分析的轨迹分析行为识别方法[J]. 电子学报, 2016, 44(1): 143-149. TIAN Guo-hui, YIN Jian-qin, YAN Yun-zhang, LI Guo-dong. Gaussian Mixture Models and Principal Component Analysis Based Human Trajectory Behavior Recognition. Chinese Journal of Electronics, 2016, 44(1): 143-149.

链接本文:

http://www.ejournal.org.cn/CN/10.3969/j.issn.0372-2112.2016.01.021 或 http://www.ejournal.org.cn/CN/Y2016/V44/I1/143

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