Object tracking by kalman filtering and recursive least squares based on 2D image motion
Yi-Wei, Feng1; Ge, Guo2; Chao-Qun, Zhu1
2008
会议名称2008 International Symposium on Computational Intelligence and Design, ISCID 2008
会议录名称Proceedings of the 2008 International Symposium on Computational Intelligence and Design, ISCID 2008
卷号2
页码106-109
会议日期October 17, 2008 - October 17, 2008
会议地点Wuhan, China
出版者IEEE Computer Society
摘要This paper proposes a novel tracking strategy that can robustly track an object within a fixed environment. We define a robust model-based tracker using kalman filtering combined with recursive least squares. The tracking is done by fitting successively more elaborate models on the tracked region and the segmentation is done by extracting the regions of the image that are consistent with the computed model of the target. We adopt a competitive and efficient dynamic kalman filtering to adaptively update the object model by adding new stable features as well as deleting inactive features. The approach is implemented on FIRA Mirosot and tested in the context of ball tracking in the FIRA domain. The implementation of our approach has been proven to be efficient and robust. © 2008 IEEE.
关键词Artificial intelligence Image segmentation Tracking (position) 2D images Ball tracking Kalman-filtering Object model Object Tracking Recursive least square (RLS) Robust modeling Tracking strategies
收录类别EI
语种英语
EI入藏号20101212787215
EI主题词Kalman filters
来源库Compendex
分类代码723.4 Artificial Intelligence
文献类型会议论文
条目标识符https://ir.lut.edu.cn/handle/2XXMBERH/116957
专题教务处(创新创业学院)
电气工程与信息工程学院
作者单位1.College of Electrical and Information Engineering, LanZhou University of Technology, 730050, China;
2.School of Information Science and Technology, DaLinan Maritime University, 116026, China
第一作者单位兰州理工大学
推荐引用方式
GB/T 7714
Yi-Wei, Feng,Ge, Guo,Chao-Qun, Zhu. Object tracking by kalman filtering and recursive least squares based on 2D image motion[C]:IEEE Computer Society,2008:106-109.
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