[1]陈婷婷,阮秋琦,安高云.视频中人体行为的慢特征提取算法[J].智能系统学报,2015,10(03):381-386.[doi:10.3969/j.issn.1673-4785.201407002]
 CHEN Tingting,RUAN Qiuqi,AN Gaoyun.Slow feature extraction algorithm of human actions in video[J].CAAI Transactions on Intelligent Systems,2015,10(03):381-386.[doi:10.3969/j.issn.1673-4785.201407002]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年03期
页码:
381-386
栏目:
学术论文—智能系统
出版日期:
2015-06-25

文章信息/Info

Title:
Slow feature extraction algorithm of human actions in video
作者:
陈婷婷12 阮秋琦1 安高云1
1. 北京交通大学 信息科学研究所, 北京 100044;
2. 北京交通大学 现代信息科学和网络技术北京市重点实验室, 北京 100044
Author(s):
CHEN Tingting12 RUAN Qiuqi1 AN Gaoyun1
1. Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China;
2. Beijing Key Laboratory of Advanced Information Science and Network Technology, Beijing Jiaotong University, Beijing 100044, China
关键词:
人体行为训练立方体慢特征函数慢特征帧间差分法
Keywords:
human actiontraining cuboidsslow feature functionslow featureframe difference
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201407002
文献标志码:
A
摘要:
从复杂的人体行为中提取出重要的有区分力的特征是进行人体行为分析的关键.目前经典的特征分析方法大多是线性的特征分析技术,对于非线性处理会导致错误的结果,为此,提出了一种慢特征提取方法.首先,利用帧间差分法获取帧差图像序列,对选定的初始帧进行特征点检测;然后,利用光流法对特征点进行跟踪,收集训练立方体;最后,利用收集的训练立方体进行慢特征函数的机器学习,提取出慢特征并进行特征表示.实验中提取每种行为的慢特征进行对比,结果显示提取的慢特征随时间变化非常缓慢,并且在不同行为之间具有很强的区分力,表明该方法能够有效提取出人体行为的慢特征.
Abstract:
Extracting important and distinguishable features from complex human actions is the key for human actions analysis. In recent years, classical feature analysis methods are mostly linear feature analysis technologies, which result in error results for non-linear processing. This paper proposes a method of extracting slow features. First, the image sequence of frame difference was obtained by the difference between the consecutive frames and some feature points of selected beginning frame were detected. Next, the feature points were tracked by optical flow method and the training cuboids were collected. Finally, the slow feature functions were learned with the collected training cuboids, then the slow features could be extracted and represented. In the experiment, slow features of each action were extracted and compared with each other. The results show that the extracted slow features vary slowly with time and action interclass has good discrimination, which suggests that this method can extract slow features from human actions effectively.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-7-2;改回日期:。
基金项目:国家“973”计划项目 (2012CB316304);国家自然科学基金资助项目(61172128);教育部创新团队发展计划项目(IRT201206).
作者简介:陈婷婷,女,1987年生,硕士研究生,主要研究方向为人体行为分析.阮秋琦,男,1944年生,教授,博士生导师,主要研究方向为数字图像处理、计算机视觉.曾多次获得省部级科技进步奖,发表学术论文350余篇,出版专著4部.安高云,男,1980年生,副教授,主要研究方向为图像处理、人脸识别、统计模式识别.
通讯作者:陈婷婷. E-mail: nuan8feng@126.com.
更新日期/Last Update: 2015-07-15