标题:Articulated hand tracking from single depth images using Gaussian Swarm Filtering
作者:Li, Dongnian ;Zhou, Yiqi
通讯作者:Zhou, Yiqi
作者机构:[Li, Dongnian ;Zhou, Yiqi ] Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan, Chi 更多
会议名称:9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
会议日期:9 June 2014 through 11 June 2014
来源:Proceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications, ICIEA 2014
出版年:2014
页码:2065-2070
DOI:10.1109/ICIEA.2014.6931510
关键词:articulated hand tracking; Gaussian particle swarm optimization; particle filter; single depth images
摘要:Articulated hand tracking from video sequences is a challenging task which is often addressed in a particle filter framework. As it is difficult to perform dense sampling in a high-dimensional hand state space, the traditional particle filter can't track articulated hand motion well. In this paper, we propose a new algorithm which combines an improved Gaussian particle swarm optimization (Gaussian PSO) with a particle filter and use the new algorithm, termed Gaussian Swarm Filtering, to track articulated hand motion from single depth images obtained by a Kinect sensor. The improved Gaussian PSO is employed to move the particles towards the promising areas in the state space based on the newest observation. By using the depth information as the only input, our method is immune to background and illumination changes. An implementation of the proposed method is developed with OpenSceneGraph (OSG). Experiments based on synthetic data and real image sequences are both performed for evaluation. The results show that the proposed method is accurate and robust for articulated hand motion tracking. © 2014 IEEE.
收录类别:EI;SCOPUS
Scopus被引频次:1
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84912099130&doi=10.1109%2fICIEA.2014.6931510&partnerID=40&md5=aec49856333844c5c9d9326e0e05c362
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