采用PHOG融合特征和多类别Adaboost分类器的行为识别  

Using PHOG fusion features and multi-class Adaboost classifier for human behavior recognition

作　　者：马世伟[1] 刘丽娜[1,2] 傅琪[1] 温加睿 MA Shi-wei;LIU Li-na;FU Qi;WEN Jia-rui(School of Mechatronic Engineering and Automation,Shanghai University, Shanghai 210072, China;School of Electrical and Electronic Engineering,Shandong University of Technology, Zibo 255049, China)

机构地区：[1]上海大学机电工程与自动化学院,上海210072 [2]山东理工大学电气与电子工程学院,山东淄博255049

出　　处：《光学精密工程》2018年第11期2827-2837,共11页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.61671285);山东省自然科学基金资助项目(No.ZR2016FP04)

摘　　要：为了解决类能量图易受人体运动时间和位置移动等因素影响而难以有效描述动作细节特征的问题,本文提出了一种基于类能量图金字塔梯度直方图(PHOG)融合特征和多类别Adaboost分类器的人体行为识别方法。该方法首先对经过躯体配准的运动人体目标轮廓图像构造平均运动能量图(AMEI)和增强的运动能量图(EMEI),分别提取其分层梯度方向直方图(PHOG)特征并进行串联融合,作为一种多层次的行为特征描述;然后使用基于查找表的LUT-Real Adaboost算法设计多类别分类器,实现图像中人体行为动作的识别。实验结果显示其在典型的人体动作数据集DHA上的正确识别率达97.6%,高于其它采用单一特征描述和SVM等分类器的方法。表明该方法将整体与局部特征相结合,可以有效描述不同尺度下的动作细节特征,增强了人体行为特征的描述能力,提高了识别性能。In order to solve the problem that energy image species(EIS)are susceptible to human movement time and position shift,i.e.,it is difficult to describe the details of human behaviors,in this paper a method of human behavior recognition was present based on pyramid gradient histogram(PHOG)fusion features and a multi-class Adaboost classifier.This method first calculated the average motion energy image(AMEI)and the enhanced motion energy image(EMEI)of an object s silhouette images after human body registration,and then it extracted the PHOG features of AMEI and EMEI and series them together to form a kind of multi-level feature descriptor of human behavior.Finally,a look-up table-based real Adaboost(LUT-Real Adaboost)algorithm was utilized to realize human behavior recognition by designing a multi-class classifier.Experimental results show that the correct recognition rate in typical depth-included human action datasets is97.6%by using this method,which is higher than that of other classifiers using single feature description and support vector machine.This reveals that,by combining global and local features,the proposed method can effectively describe the detailed active features of human behavior at different scales,enhance the description ability of human behavior characteristics,and improve recognition performance.

关 键 词：人体行为识别 平均运动能量图 增强运动能量图 分层梯度方向直方图特征 查找表型RealAdaboost 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]