High-quality region-based foreground segmentation using a spatial grid of SVM classifiers
Xiaohan Zhang, Carlos Roberto Del-Blanco, Carlos Cuevas, Fernando Jaureguizar, Narciso N. Garcia · 2014
This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.