We present a background subtraction algorithm aimed at efficiency and robustness to common sources of disturbance such as illumination changes, camera gain and exposure variations, noise. The approach relies on modeling the local effect of disturbance factors on a neighborhood of pixel intensities as a second-degree polynomial transformation plus additive Gaussian noise. This allows for classifying pixels as changed or unchanged by a simple least-squares polynomial fitting procedure. Experimental results prove that the approach is state-of-the-art in challenging sequences characterized by sources of disturbance yielding sudden and strong background appearance changes.

Robust and efficient background subtraction by quadratic polynomial fitting

LANZA, ALESSANDRO;TOMBARI, FEDERICO;DI STEFANO, LUIGI
2010

Abstract

We present a background subtraction algorithm aimed at efficiency and robustness to common sources of disturbance such as illumination changes, camera gain and exposure variations, noise. The approach relies on modeling the local effect of disturbance factors on a neighborhood of pixel intensities as a second-degree polynomial transformation plus additive Gaussian noise. This allows for classifying pixels as changed or unchanged by a simple least-squares polynomial fitting procedure. Experimental results prove that the approach is state-of-the-art in challenging sequences characterized by sources of disturbance yielding sudden and strong background appearance changes.
IEEE Proceedings of 17th IEEE International Conference on Image Processing (ICIP)
1537
1540
A. Lanza; F. Tombari; L. Di Stefano
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/96875
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