[1]庄伟源,成运,林贤明,等.关键肢体角度直方图的行为识别[J].智能系统学报,2015,10(01):20-26.[doi:10.3969/j.issn.1673-4785.201410039]
 ZHUANG Weiyuan,CHENG Yun,LIN Xianming,et al.Action recognition based on the angle histogram of key parts[J].CAAI Transactions on Intelligent Systems,2015,10(01):20-26.[doi:10.3969/j.issn.1673-4785.201410039]
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关键肢体角度直方图的行为识别(/HTML)
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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01期
页码:
20-26
栏目:
出版日期:
2015-03-25

文章信息/Info

Title:
Action recognition based on the angle histogram of key parts
作者:
庄伟源13 成运2 林贤明13 苏松志13 曹冬林13 李绍滋13
1. 厦门大学 信息科学与技术学院, 福建 厦门 361005;
2. 湖南人文科技学院 通信与控制工程系, 湖北 娄底 417000;
3. 福建省仿脑智能系统重点实验室, 福建 厦门 361005
Author(s):
ZHUANG Weiyuan13 CHENG Yun2 LIN Xianming13 SU Songzhi13 CAO Donglin13 LI Shaozi13
1. School of Information Science and Technology, Xiamen University, Xiamen 361005, China;
2. Department of Communication and Control Engineering, Hunan University of Humanities, Science and Technology, Loudi 417000, China;
3. Fujian Key Laboratory of the Brain-Like Intelligent Systems, Xiamen 361005, China
关键词:
角度特征动作识别关键肢体角度直方图姿态表示行为分析动作特征
Keywords:
angle featureaction recognitionkey partsangle histogrampose representationaction analyzeaction feature
分类号:
TP391.4
DOI:
10.3969/j.issn.1673-4785.201410039
文献标志码:
A
摘要:
当前的姿态表示的行为识别方法通常对姿态的准确性做了很强的假设,而当姿态分析不精确时,这些现有方法的识别效果不佳。提出了一种低维的、鲁棒的基于关键肢体角度直方图的人体姿态特征描述子,用于将整个动作视频映射成一个特征向量。同时,还在特征向量中引入共生模型,用以表示肢体间的关联性。最后,设计了分层的SVM分类器,第1层主要用于选择高判别力的肢体作为关键肢体,第2层则利用关键肢体的角度直方图并作为特征向量,进行行为识别。实验结果表明,基于关键肢体角度直方图的动作特征具有较好的判别能力,能更好地区分相似动作,并最终取得了更好的识别效果。
Abstract:
The current pose-based methods usually make a strong assumption for the accuracy of pose, but when the pose analysis is not precise, these methods cannot achieve satisfying results of recognition. Therefore, this paper proposed a low-dimensional and robust descriptor on the gesture feature of the human body based on the angle histogram of key limbs, which is used to map the entire action video into an feature vector. A co-occurrence model is introduced into the feature vector for expressing the relationship among limbs. Finally, a two-layer support vector machine (SVM) classifier is designed. The first layer is used to select highly discriminative limbs as key limbs and the second layer takes angle histogram of key limbs as the feature vector for action recognition. Experiment results demonstrated that the action feature based on angle histogram of key limbs has excellent judgment ability, may properly distinguish similar actions and achieve better recognition effect.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-10-24;改回日期:。
基金项目:国家自然科学基金资助项目(61202143);福建省自然科学基金资助项目(2013J05100,2010J01345,2011J01367);厦门市科技重点项目资助项目(3502Z20123017).
作者简介:庄伟源,男,1990年生,硕士研究生,主要研究方向为人体行为识别、计算机视觉、深度学习;林贤明,男,1980年生,助理教授,博士,主要研究方向为人体行为识别、移动视觉搜索、计算机视觉、模式识别;李绍滋,男,1963年生,教授,博士生导师,博士,福建省人工智能学会副理事长兼秘书长,主要研究方向为运动目标检测与识别、自然语言处理与多媒体信息检索等。发表学术论文160余篇,其中被SCI检索16篇、被EI检索142篇。
通讯作者:林贤明.E-mail:linxm@xmu.edu.cn.
更新日期/Last Update: 2015-06-16