Lanzhou University of Technology Institutional Repository (LUT_IR)
Object tracking by kalman filtering and recursive least squares based on 2D image motion | |
Yi-Wei, Feng1![]() ![]() | |
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
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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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