Accurate and Efficient Background Subtraction by Monotonic Second-Degree Polynomial Fitting

We present a background subtraction approach aimedat efficiency and accuracy also in presence of commonsources of disturbance such as illumination changes, cameragain and exposure variations, noise. The novelty ofthe proposal relies on a-priori modeling the local effect ofdisturbs on small neighborhoods of pixel intensities as amonotonic, homogeneous, second-degree polynomial transformationplus additive Gaussian noise. This allows forclassifying pixels as changed or unchanged by an efficientinequality-constrained least-squares fitting procedure. Experimentsprove that the approach is state-of-the-art interms of efficiency-accuracy tradeoff on challenging sequencescharacterized by disturbs yielding sudden andstrong variations of the background appearance.