Multi-scale Fusion of Texture and Color for Background Modeling

Abstract

Background modeling from a stationary camera is a crucial component in video surveillance. Traditional methods usually adopt single feature type to solve the problem, while the performance is usually unsatisfactory when handling complex scenes. In this paper, we propose a multi-scale strategy, which combines both texture and color features, to achieve a robust and accurate solution. Our contributions are two folds: one is that we propose a novel texture operator named Scale-invariant Center-symmetric Local Ternary Pattern, which is robust to noise and illumination variations, the other is that a multi-scale fusion strategy is proposed for the issue. Our method is verified on several complex real world videos with illumination variation, soft shadows and dynamic backgrounds. We compare our method with four state-of-the-art methods, and the experimental results clearly demonstrate that our method achieves the highest classification accuracy in complex real world videos.