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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电子学报 ›› 2016, Vol. 44 ›› Issue (1) : 143-149. DOI: 10.3969/j.issn.0372-2112.2016.01.021

学术论文

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

田国会¹, 尹建芹^1,2, 闫云章¹, 李国栋¹

作者信息 +

Gaussian Mixture Models and Principal Component Analysis Based Human Trajectory Behavior Recognition

TIAN Guo-hui¹, YIN Jian-qin^1,2, YAN Yun-zhang¹, LI Guo-dong¹

Author information +

文章历史 +

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.

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.

导出引用

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. https://doi.org/10.3969/j.issn.0372-2112.2016.01.021

TIAN Guo-hui, YIN Jian-qin, YAN Yun-zhang, LI Guo-dong. Gaussian Mixture Models and Principal Component Analysis Based Human Trajectory Behavior Recognition[J]. Acta Electronica Sinica, 2016, 44(1): 143-149. https://doi.org/10.3969/j.issn.0372-2112.2016.01.021

中图分类号： TP391

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