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

We present a background subtraction approach aimed at efficiency and accuracy also in presence of common sources of disturbance such as illumination changes, camera gain and exposure variations, noise. The novelty of the proposal relies on a-priori modeling the local effect of disturbs on small neighborhoods of pixel intensities as a monotonic, homogeneous, second-degree polynomial transformation plus additive Gaussian noise. This allows for classifying pixels as changed or unchanged by an efficient inequality-constrained least-squares fitting procedure. Experiments prove that the approach is state-of-the-art in terms of efficiency-accuracy tradeoff on challenging sequences characterized by disturbs yielding sudden and strong variations of the background appearance.