Online Sparse IR Background Estimation via KRLS

摘要：

Background estimation is the first step of background suppression in many infrared (IR) target detection algorithms. One sort of these algorithms consider background estimation as a supervised learning problem. On this point of view, it is necessary to search sparse solutions to control the complexity of the learned function to achieve good generalization. On the other hand, the more effective nonlinear regression algorithms are computationally demanding, so it is required to operate online. In this paper, a nonlinear online IR image background estimation algorithm based on sparse Kernel Recursive Least Squares (KRLS) is proposed. Nonlinear function regression and real IR image data experiments are performed; the results of these experiments are compared to that of original Least Squares (LS), 2-D Least Mean Squares (TDLMS) and the kernel version of LS (KLS) algorithm. The feasibility of nonlinear function regression and background estimation via this algorithm is thus demonstrated.

关键词： sequence IR images supervised learning model background estimation online sparse kernel RLS

作者: Bin Zhu Zhengdong Cheng Xiang Fan Huachun Tan

作者单位: State Key Laboratory of Pulsed Power Laser Technology Electronic Engineering Institute Hefei,P.R. Ch Department of Transportation Engineering Beijing Institute of Technology Beijing,P.R.China,100081

会议类型: 国际会议

会议名称: 2010 IEEE信息与自动化国际会议(ICIA 2010)

会议地点: 哈尔滨

会议语种:英文

页码: 1-5

在线出版日期: 2010-06-20（万方平台首次上网日期，不代表论文的发表时间）