[1]龚冬颖,黄敏,张洪博,等.RGBD人体行为识别中的自适应特征选择方法[J].智能系统学报,2017,12(1):1-7.[doi:10.11992/tis.201611008]
　GONG Dongying,HUANG Min,ZHANG Hongbo,et al.Adaptive feature selection method for action recognition of human body in RGBD data[J].CAAI Transactions on Intelligent Systems,2017,12(1):1-7.[doi:10.11992/tis.201611008]
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RGBD人体行为识别中的自适应特征选择方法

《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷: 12 期数: 2017年第1期 页码: 1-7 栏目: 学术论文—机器感知与模式识别 出版日期: 2017-02-25
Title:
Adaptive feature selection method for action recognition of human body in RGBD data
作者:
龚冬颖1,2, 黄敏1,2, 张洪博3, 李绍滋1,2
1. 厦门大学 智能科学与技术系, 福建 厦门 361005;
2. 厦门大学 福建省仿脑智能系统重点实验室, 福建 厦门 361005;
3. 华侨大学 计算机科学与技术学院, 福建 厦门 361005
Author(s):
GONG Dongying1,2, HUANG Min1,2, ZHANG Hongbo3, LI Shaozi1,2
1. Intelligent Science & Technology Department, Xiamen University, Xiamen 361005, China;
2. Fujian Key Laboratory of Brain-like Intelligent Systems, Xiamen University, Xiamen 361005, China;
3. Computer Science & Technology School, Huaqiao University, Xiamen 361005, China
关键词:
人体行为识别; 自适应特征选择; 信息熵; 随机森林
Keywords:
action recognition of human body; adaptive feature selection; information entropy; random forest
分类号:
TP391.41
DOI:
10.11992/tis.201611008
摘要:
目前在RGBD视频的行为识别中，为了提高识别准确率，许多方法采用多特征融合的方式。通过实验分析发现，行为在特定特征上的分类效果好，但是多特征融合并不能体现个别特征的分类优势，同时融合后的特征维度很高，时空开销大。为了解决这个问题，提出了RGBD人体行为识别中的自适应特征选择方法，通过随机森林和信息熵分析人体关节点判别力，以高判别力的人体关节点的数量作为特征选择的标准。通过该数量阈值的筛选，选择关节点特征或者关节点相对位置作为行为识别特征。实验结果表明，该方法相比于特征融合的算法，行为识别的准确率有了较大提高，超过了大部分算法的识别结果。
Abstract:
Many methods adopt the technique of multi-feature fusion to improve the recognition accuracy of RGBD video. Experimental analyses revealed that the classification effect of certain behavior in some features is good; however, multi-feature fusion cannot reflect the classification superiority of certain features. Moreover, multi-feature fusion is highly dimensional and considerably expensive in terms of time and space. This research proposes an adaptive feature selection method for RGBD human-action recognition to solve this problem. First, random forest and information entropy were used to analyze the judgment ability of the human joints, whereas the number of human joints with high judgment ability were chosen as the feature selection criterion. By screening the threshold number, either the joint feature or the relative positions of the joints was used as the recognition feature of action. Experimental results show that compared with multi-feature fusion, the method significantly improved the accuracy of action recognition and outperformed most other algorithms.
参考文献/References:
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备注/Memo

收稿日期:2016-11-7;改回日期:。
基金项目:国家自然科学基金项目（61572409，61571188，61202143）；福建省自然科学基金项目（2013J05100）；中医健康管理福建省2011协同创新中心项目.
作者简介:龚冬颖,女,1992年生,硕士研究生,主要研究方向为行为识别、机器学习;黄敏,女,1982年生,博士研究生,主要研究方向为行为识别、机器学习、目标检测和图像检索;张洪博,男,1986年生,讲师,博士,主要研究方向为人体行为识别,主持国家自然科学基金青年项目和福建省自然科学基金面上项目各1项,发表学术论文多篇,其中被SCI、EI检索20余篇。
通讯作者:李绍滋.E-mail:szlig@xmu.edu.cn.

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