计算机技术与发展2018，Vol.28Issue(6)：30-34,5.DOI:10.3969/j.issn.1673-629X.2018.06.007
基于多特征的深度图像序列人体行为识别
Human Activity Recognition from Depth Image Sequences Based on Multiple Features
摘要
Abstract
The current methods cannot yield satisfactory performance since they just employ single feature for research of human activity recognition from depth image sequences. For this,we propose a novel method based on multiple features including the super normal vector features and the histograms of oriented gradients features from the depth motion maps. Firstly,in order to increase the complementarities of features,we extract two types of features from depth image sequences including the super normal vector features and the histograms of oriented gradients features from the depth motion maps. Then we use the kernel extreme learning machine to obtain the recognition result of two features. Finally,the rule of logarithmic opinion pool is used to combine the classification outcomes. The tests on MSR Action3D dataset show that it can achieve recognition ratio of 96. 3％ which is higher not only than that of the methods based on the super normal vector features and based on the histograms of oriented gradients features from the depth motion maps,but also than that of other meth-ods.关键词
人体行为识别/深度图像序列/多特征/核极限学习机Key words
human activity recognition/depth image sequences/multiple featured/kernel extreme learning machine分类
信息技术与安全科学引用本文复制引用
宋相法,姚旭..基于多特征的深度图像序列人体行为识别[J].计算机技术与发展,2018,28(6):30-34,5.基金项目
国家自然科学基金(U1504611) （U1504611）
河南省教育科学技术研究重点项目(15A520010) （15A520010）
