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Robust human action recognition using dynamic movement features
Zhang HW(张会文); Fu ML(付明亮); Luo HT(骆海涛); Zhou WJ(周维佳)
作者部门空间自动化技术研究室
会议名称10th International Conference on Intelligent Robotics and Applications, ICIRA 2017
会议日期August 16-18, 2017
会议地点Wuhan, China
会议录名称Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版者Springer Verlag
出版地Berlin
2017
页码474-484
收录类别EI
EI收录号20173504107696
产权排序1
ISSN号0302-9743
ISBN号978-3-319-65288-7
关键词Action Recognition Dmp Dtw
摘要Action recognition has been widely researched in video surveillance, auxiliary medical care and robotics. In the context of robotics, in order to program robots by demonstration (PbD), we not only need our algorithms to be capable of identifying different actions, but also to be able to encode and reproduce them. Dynamic movement primitives (DMPs), as a trajectory encoding method, are widely used in motion synthesize and generation. But at the same time it can also be applied to action recognition. With this idea, this paper extracts a kind of dynamic features from the original trajectory within DMP framework. The feature is temporal-spatial invariant. Based on the feature, FastDTW-KNN algorithm is proposed to solve the recognition task. Experiments tested on HAR dataset and handwritten letters dataset achieved an excellent recognition performance under a large data noise, which has verified the effectiveness of our method. In addition, comparative recognition experiments based on the original feature and our extracted dynamic feature are conducted. Results show that the dynamic feature is robust under temporal and spatial noise. As for classifiers, we compared our method with KNN, SVM and DTW-KNN followed with a detailed analysis of their advantages and disadvantages.
语种英语
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/20868
专题空间自动化技术研究室
通讯作者Zhang HW(张会文)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, 110016, China
2.University of Chinese Academy of Science, Beijing, China
推荐引用方式
GB/T 7714
Zhang HW,Fu ML,Luo HT,et al. Robust human action recognition using dynamic movement features[C]. Berlin:Springer Verlag,2017:474-484.
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