标题:Action recognition by extracting pyramidal motion features from skeleton sequences
作者:Lu, Guoliang ;Zhou, Yiqi ;Li, Xueyong ;Lv, Chen
作者机构:[Lu, Guoliang ;Zhou, Yiqi ;Li, Xueyong ] Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE, China;[Lv, Chen ] Institute of Com 更多
来源:Lecture Notes in Electrical Engineering
出版年:2015
卷:339
页码:251-258
DOI:10.1007/978-3-662-46578-3_29
关键词:Action features; Action recognition; Motion skeletons
摘要:Human action recognition has been a long-standing problem in computer vision. Computational efficiency is an important aspect in the design of an action-recognition based practical system. This paper presents a framework for efficient human action recognition. The novel pyramidal motion features are proposed to represent skeleton sequences via computing position offsets in 3D skeletal body joints. In the recognition phase, a Naive-Bayes-Nearest-Neighbors (NBNN) classifier is used to take into account the spatial independence of body joints.We conducted experiments to systematically test our framework on the public UCF dataset. Experimental results show that, compared with the state-ofthe- art approaches, the presented framework is more effective and more accurate for action recognition, and meanwhile it has a high potential to be more efficient in computation. © Springer-Verlag Berlin Heidelberg 2015.
收录类别:EI;SCOPUS
Scopus被引频次:3
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84923207013&doi=10.1007%2f978-3-662-46578-3_29&partnerID=40&md5=640a96038f17046f68ca0f08cc5c4e21
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