Adaptive difference modelling for background subtraction

Xianghao Zang, Ge Li, Jun Yang, Wenmin Wang

Abstract: Background subtraction plays a very important role in video analysis, especially in surveillance systems. While being straightforward, the performance based on frame differencing is unsatisfied due to its sensitiveness to issues such as camera shake and swinging objects. To address its limitations, in this paper we propose a complete adaptive difference modelling framework. First, we introduce two difference discriminators to model the evolution process of pixels. Second, we use Gaussian Mixture Models to adaptively learn the difference threshold to distinguish foreground from background. Third, three heuristics are employed to further improve the model adaptability. Experiments on real-world videos of the Background Models Challenge (BMC) demonstrate that our method performs better on global quality metric (FSD) than other state-of-the-art methods.