[1]姬晓飞,王昌汇,王扬扬.分层结构的双人交互行为识别方法[J].智能系统学报编辑部,2015,10(6):893-900.[doi:10.11992/tis.201505006]
 JI Xiaofei,WANG Changhui,WANG Yangyang.Human interaction behavior-recognition method based on hierarchical structure[J].CAAI Transactions on Intelligent Systems,2015,10(6):893-900.[doi:10.11992/tis.201505006]
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分层结构的双人交互行为识别方法(/HTML)
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《智能系统学报》编辑部[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年6期
页码:
893-900
栏目:
出版日期:
2015-12-25

文章信息/Info

Title:
Human interaction behavior-recognition method based on hierarchical structure
作者:
姬晓飞 王昌汇 王扬扬
沈阳航空航天大学自动化学院, 辽宁沈阳 110136
Author(s):
JI Xiaofei WANG Changhui WANG Yangyang
School of Automation, Shenyang Aerospace University, Shenyang 110136, China
关键词:
计算机视觉交互动作动作识别方向梯度直方图分层模型最近邻分类器UT-interaction数据库加权融合
Keywords:
computer visionhuman interactionaction recognitionhistogram of oriented gradientlayered modelnearest neighbor classifierut-interaction databaseweighted fusion
分类号:
TP391.4
DOI:
10.11992/tis.201505006
摘要:
针对日常生活中双人交互行为因运动区域难以分割,造成无法准确识别的问题,提出了一种基于分层结构的双人交互行为识别方法。该方法首先按照交互行为双方身体是否接触作为分界点,将整个交互行为分为开始阶段、执行阶段和结束阶段。将开始阶段与结束阶段左右两侧人体所在矩形区域分别提取作为该兴趣区域,将执行阶段双人所在矩形区域整体提取作为感兴趣区域,分别提取HOG特征。使用1NN分类器获得每个阶段的每个对象的识别概率,最终通过加权融合各个阶段各个对象的识别概率实现对该交互行为的识别。利用UT-interaction数据库对该方法进行测试的实验结果表明,该方法实现简单,并具有良好的识别效果。
Abstract:
To solve the problem of double interaction behavior recognition, in this paper we propose a novel interaction behavior-recognition method based on hierarchical structure. First, the interactive behavior of both areas of body contact is determined as the cut-off point. The interaction process is then divided into three stages-the start, execution, and end stages. We extract the left and right human body regions in the start and end stages, respectively. Both human body regions are extracted as a whole in the execute stage. Next, we utilize a histogram of oriented gradients(HOG) descriptor to describe information on the regions of interest of each stage. Thereafter, we use the nearest neighbor classifier to obtain the recognition probability of each object in each stage. Finally, we obtain the recognition result from the weighted fusion of this recognition probability. The experimental results, using the UT-interaction dataset, demonstrate that the proposed approach is easy to implement and has good recognition effect.

参考文献/References:

[1] WEINLAND D, RONFARD R, BOYER E. A survey of vision-based methods for action representation, segmentation and recognition[J]. Computer Vision and Image Understanding, 2011, 115(2):224-241.
[2] SEO H J, MILANFAR P. Action recognition from one example[J]. IEEE Transactions on pattern Analysis and Machine Intelligence, 2011, 33(5):867-882.
[3] 吴联世, 夏利民, 罗大庸. 人的交互行为识别与理解研究综述[J]. 计算机应用与软件, 2011, 28(11):60-63. WU Lianshi, XIA Limin, LUO Dayong. Survey on human interactive behaviour recognition and comprehension[J]. Computer Applications and Software, 2011, 28(11):60-63.
[4] YU Gang, YUAN Junsong, LIU Zicheng. Propagative hough voting for human activity recognition[C]//Proceedings of the 12th European Conference on Computer Vision, Florence, Italy. Berlin Heidelberg:Springer, 2012:693-706.
[5] YU T H, KIM T K, CIPOLLA R. Real-time action recognition by spatiotemporal semantic and structural forests[C]//Proceedings of the 21st British Machine Vision Conference. United Kingdom, 2010:1-12.
[6] YUAN Fei, SAHBI H, PRINET V. Spatio-temporal context kernel for activity recognition[C]//Proceedings of the 1st Asian Conference on Pattern Recognition. Beijing, China, 2011:436-440.
[7] BURGHOUTS G J, SCHUTTE K. Spatio-temporal layout of human actions for improved bag-of-words action detection[J]. Pattern Recognition Letters, 2013, 34(15):1861-1869.
[8] LI Nijun, CHENG Xu, GUO Haiyan,et al. A hybrid method for human interaction recognition using spatio-temporal interest points[C]//Proceedings of the 22nd International Conference on Pattern Recognition. Stockholm, Sweden, 2014:2513-2518.
[9] KONG Yu, JIA Yunde, FU Yun. Interactive phrases:semantic descriptions for human interaction recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(9):1775-1788.
[10] SLIMANI K, BENEZETH Y, SOUAMI F. Human interaction recognition based on the co-occurrence of visual words[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, Ohio, USA, 2014:461-466.
[11] JIANG Zhuolin, LIN Zhe, DAVIS L S. Recognizing human actions by learning and matching shape-motion prototype trees[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3):533-547.
[12] JI Xiaofei, ZHOU Lu, LI Yibo. Human action recognition based on adaboost algorithm for feature extraction[C]//Proceedings of 2014 IEEE International Conference on Computer and Information Technology. Xi’an, China, 2014:801-805.
[13] RYOO M S, AGGARWAL J K. Spatio-temporal relationship match:Video structure comparison for recognition of complex human activities[C]//Proceedings of the IEEE International Conference on Computer Vision. Kyoto, Japan, 2009:1593-1600.
[14] KONG Y, LIANG W, DONG Z, et al. Recognising human interaction from videos by a discriminative model[J]. Institution of Engineering and Technology Computer Vision, 2014, 8(4):277-286.
[15] PATRON-PEREZ A, MARSZALEK M, REID I, et al. Structured learning of human interactions in TV shows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(12):2441-2453.
[16] MUKHERJEE S, BISWAS S K, MUKHERJEE D P. Recognizing interaction between human performers using "key pose doublet"[C]//Proceedings of the 19th ACM International Conference onMultimedia. Scottsdale, AZ, United states, 2011:1329-1332.
[17] BRENDEL W, TODOROVIC S. Learning spatiotemporal graphs of human activities[C]//Proceedings of the IEEE International Conference on Computer Vision. Barcelona, Spain, 2011:778-785.

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备注/Memo

备注/Memo:
收稿日期:2015-05-06;改回日期:。
基金项目:国家自然科学基金资助项目(61103123);辽宁省高等学校优秀人才支持计划资助项目(LJQ2014018);辽宁省教育厅科技研究基金资助项目(L2014066).
作者简介:姬晓飞,女,1978年生,副教授,博士,主要研究方向为视频分析与处理、模式识别理论。承担国家自然科学基金、教育部留学回国启动基金等多项课题研究。发表学术论文40余篇,其中被SCI、EI检索20余篇。参与编著英文专著1部。王昌汇,男,1991年出生,硕士研究生,主要研究方向为基于图模型的双人交互行为识别。王扬扬,女,1979年生,工程师,博士,主要研究方向为视频分析与处理、模式识别理论。承担辽宁省教育厅科学研究一般项目等多项课题研究。发表学术论文20余篇,其中被SCI、EI检索8篇。
通讯作者:姬晓飞.E-mail:jixiaofei7804@126.com.
更新日期/Last Update: 1900-01-01