Robust human action recognition using dynamic movement features
文献类型：会议论文
| 作者 | Zhang HW(张会文) ; Fu ML(付明亮); Luo HT(骆海涛)
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| 出版日期 | 2017 |
| 会议日期 | August 16-18, 2017 |
| 会议地点 | Wuhan, China |
| 关键词 | Action Recognition Dmp Dtw |
| 页码 | 474-484 |
| 英文摘要 | 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. |
| 产权排序 | 1 |
| 会议录 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
 |
| 会议录出版者 | Springer Verlag |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 978-3-319-65288-7 |
| 源URL | [http://ir.sia.cn/handle/173321/20868]  |
| 专题 | 沈阳自动化研究所_空间自动化技术研究室 |
| 通讯作者 | Zhang HW(张会文) |
| 作者单位 | 1.University of Chinese Academy of Science, Beijing, China 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, 110016, China |
| 推荐引用方式 GB/T 7714 | Zhang HW,Fu ML,Luo HT. Robust human action recognition using dynamic movement features[C]. 见:. Wuhan, China. August 16-18, 2017. |
入库方式： OAI收割
来源：沈阳自动化研究所
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