Background Estimation in Kernel Space

One problem in background estimation is the inherent change in the background such as waving tree branches, water surfaces, camera shakes, and the existence of moving objects in every image. In this paper, a new method for background estimation is proposed based on function approximation in kernel domain. For this purpose, Weighted Kernel-based Learning Algorithm (WKLA) is designed. WKLA includes a weighted type of kernel least mean square algorithm with ability to function approximation in the presence of noise. So, the proposed background estimation method includes two stages: ̄rstly, a novel algorithm for outlier detection namely Fuzzy Outlier Detector (FOD) is applied. Then obtained results are fed to the WKLA. The proposed approach can handle scenes containing moving backgrounds, gradual illumination changes, camera vibrations, and non-empty backgrounds. The qualitative results and quantitative evaluations on various indoor and outdoor sequences relative to existing approaches show the high accuracy and e®ectiveness of the proposed method in background estimation and foreground detection.