Object tracking by kalman filtering and recursive least squares based on 2D image motion

	Object tracking by kalman filtering and recursive least squares based on 2D image motion
	Yi-Wei, Feng 1; Ge, Guo 2; Chao-Qun, Zhu 1
	2008
会议日期	October 17, 2008 - October 17, 2008
会议地点	Wuhan, China
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
卷号	2
页码	106-109
英文摘要	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.
会议录	Proceedings of the 2008 International Symposium on Computational Intelligence and Design, ISCID 2008
会议录出版者	IEEE Computer Society
语种	英语
内容类型	会议论文
源URL	[http://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
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]. 见:. Wuhan, China. October 17, 2008 - October 17, 2008.