A Generic Feature Selection Method for Background Subtraction Using Global Foreground Models

Over the last few years, a wide variety of background subtraction algorithms have been proposed for the detection of moving objects in videos acquired with a static camera. While much effort have been devoted to the development of robust background models, the automatic spatial selection of useful features for representing the background has been neglected. In this paper, we propose a generic and tractable feature selection method. Interesting contributions of this work are the proposal of a selection process coherent with the segmentation process and the exploitation of global foreground models in the selection strategy. Experiments conducted on the ViBe algorithm show that our feature selection technique improves the segmentation results.