太赫兹科学与电子信息学报2016，Vol.14Issue(3)：443-448,6.DOI:10.11805/TKYDA201603.0443
基于深度信息的人体运动识别方法
Human motion recognition method based on depth information
陈力 1王俊凯 1张玉玺1
作者信息
- 1. 北京航空航天大学 电子信息工程学院，北京 100191
- 折叠
摘要
Abstract
In recent years, recognition of human motion on visible light video sequences has made some progress. Since the data sources are sensitive to target color, light intensity and background clutters, the depth information is applied to human motion recognition. In this paper, a local representation method of human movement based on Spatio-Temporal Interest Points(STIPs) is adopted, and the applications of Harris and Gabor filter detection methods on depth information are achieved. A novel Depth Cuboid Similarity Feature(DCSF) is built to describe the corresponding results. Finally, action classification is completed by Support Vector Machine(SVM) classifier based on spatio-temporal codebook. Experimental results demonstrate that detection method of Gabor filter obtains better recognition performance in depth datasets.关键词
分层传输运动分析/运动识别/时空兴趣点/运动表征/SVM分类Key words
motion analysis/motion recognition/spatio-temporal interest points/motion characterization/Support Vector Machine classification分类
信息技术与安全科学引用本文复制引用
陈力,王俊凯,张玉玺..基于深度信息的人体运动识别方法[J].太赫兹科学与电子信息学报,2016,14(3):443-448,6.
