Bayesian Order-Consistency Testing with Class Priors Derivation for Robust Change Detection

Abstract

In this paper we propose a formalization of change detection as a Bayesian order-consistency test, based on the assumption that disturbance factors such as illumination changes and variations of camera parameters do not change the ordering between noiseless intensities within a neighborhood of pixels. The assumption of additive, zero-mean, i.i.d. gaussian noise allows for testing the composite order-consistency hypothesis by efficient computation of the marginal likelihood. Moreover, since the above formalization enables to incorporate changed/unchanged class priors seamlessly, we also propose a simple method to derive informative priors based on the calculation of marginal likelihoods at reduced resolution. Experimental results on challenging test sequences characterized by sudden and strong illumination changes prove the effectiveness of the proposed approach.