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An Unsupervised Feature learning and clustering method for key frame extraction on human action recognition
Pei XM(裴晓敏); Fan BJ(范保杰); Tang YD(唐延东)
作者部门机器人学研究室
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码759-762
收录类别EI ; CPCI(ISTP)
EI收录号20183905873431
WOS记录号WOS:000447628700138
产权排序1
ISBN号978-1-5386-0489-2
关键词Human Action Recognition Learning Feature Stacked Aut-encoder Affinity Propagation Clustering
摘要

Human action recognition in video is an active research topic in computer vision. However, with the growing convenience of capturing and sharing videos, there are a growing variety of human action datasets with substantial amount of videos make human action recognition challenging problems, which can be solved by key frame extraction. Feature Clustering methods are usually employed to extract key frames. One difficulty is caused by the large variety of visual content in videos, makes hand-craft feature is not always effective, since there are no fixed descriptors can describe all video cases. Another difficulty is that traditional clustering algorithms are sensitive to the choice of initial clustering centers. An Unsupervised feature learning and clustering method is proposed for key frame extraction on human action recognition, Stacked auto-encoder(SAE) is trained using videos from 10 different human actions, after training, SAE is used as a feature extractor to learn features representing human actions. Affinity Propagation Clustering algorithm is used to select key frames from video sequences. Experiments using a variety of videos demonstrate that our method can be effectively summarizing video shots considering different human actions.

语种英语
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/21343
专题机器人学研究室
通讯作者Pei XM(裴晓敏)
作者单位State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016, China
推荐引用方式
GB/T 7714
Pei XM,Fan BJ,Tang YD. An Unsupervised Feature learning and clustering method for key frame extraction on human action recognition[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:759-762.
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